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ORCIDBao-Liang Lu
吕宝粮
2020 – today
- 2025
[c228]Yan-Kai Liu, Jinyu Cai, Bao-Liang Lu, Wei-Long Zheng:
Multi-to-Single: Reducing Multimodal Dependency in Emotion Recognition Through Contrastive Learning. AAAI 2025: 1438-1446- 2024
- [j72]Haojin Deng
, Shiqi Wang, Yimin Yang, W. G. Will Zhao, Hui Zhang, Ruizhong Wei, Q. M. Jonathan Wu, Bao-Liang Lu:
Minimizing EEG Human Interference: A Study of an Adaptive EEG Spatial Feature Extraction With Deep Convolutional Neural Networks. IEEE Trans. Cogn. Dev. Syst. 16(6): 1915-1928 (2024) - [j71]Cunbo Li, Peiyang Li, Zhaojin Chen, Lei Yang, Fali Li, Feng Wan, Zehong Cao, Dezhong Yao, Bao-Liang Lu, Peng Xu:
Brain Network Manifold Learned by Cognition-Inspired Graph Embedding Model for Emotion Recognition. IEEE Trans. Syst. Man Cybern. Syst. 54(12): 7794-7808 (2024) - [c227]Tian-Fang Ma, Xuan-Hao Liu, Wei-Long Zheng, Bao-Liang Lu:
Emotion Recognition from Eye Movements Using Multi-way Autoregressive Model. BIBM 2024: 1078-1084 - [c226]Mingyu Gou, Ying-Jie Zhang, Ren-Jie Dai, Hao-Long Yin, Tianzhen Chen, Fei Cheng, Bao-Liang Lu, Jiang Du, Wei-Long Zheng:
Addressing Temporal and Auditory Factors in Meditative EEG with Self-Supervised Learning. BIBM 2024: 1954-1959 - [c225]Xuan-Hao Liu, Wei-Bang Jiang, Wei-Long Zheng, Bao-Liang Lu:
MoGE: Mixture of Graph Experts for Cross-subject Emotion Recognition via Decomposing EEG. BIBM 2024: 3515-3520 - [c224]Khin Pa Pa Aung, Hao-Long Yin, Tian-Fang Ma, Wei-Long Zheng, Bao-Liang Lu:
A Multimodal Myanmar Emotion Dataset for Emotion Recognition. EMBC 2024: 1-4 - [c223]Haowei Cui, Hanwen Shi, Bao-Liang Lu, Wei-Long Zheng:
Improving Cross-Subject Emotion Recognition Performance with an Encoder-Decoder Structure. EMBC 2024: 1-4 - [c222]Mingyu Gou, Hao-Long Yin, Bao-Liang Lu, Wei-Long Zheng:
Multi-modal Adversarial Regressive Transformer for Cross-subject Fatigue Detection. EMBC 2024: 1-4 - [c221]Wei Gu, Yun-Huan Li, Li-Ming Zhao, Wei-Long Zheng, Bao-Liang Lu:
EEG-Based Tension Recognition Annotated with Electrodermal Activity. EMBC 2024: 1-4 - [c220]Keya Hu, Ren-Jie Dai, Wen-Tao Chen, Hao-Long Yin, Bao-Liang Lu, Wei-Long Zheng:
Contrastive Self-supervised EEG Representation Learning for Emotion Classification. EMBC 2024: 1-4 - [c219]Tian-Hua Li, Tian-Fang Ma, Dan Peng, Wei-Long Zheng, Bao-Liang Lu:
Focused State Recognition Using EEG with Eye Movement-Assisted Annotation. EMBC 2024: 1-4 - [c218]Yi-Dong Zhao, Yan-Kai Liu, Wei-Long Zheng, Bao-Liang Lu:
EEG Data Augmentation for Emotion Recognition Using Diffusion Model. EMBC 2024: 1-4 - [c217]Ziyi Li, Li-Ming Zhao, Wei-Long Zheng, Bao-Liang Lu:
Temporal-Spatial Prediction: Pre-Training on Diverse Datasets for EEG Classification. ICASSP 2024: 1806-1810 - [c216]Wei-Bang Jiang, Ziyi Li, Wei-Long Zheng, Bao-Liang Lu:
Functional Emotion Transformer for EEG-Assisted Cross-Modal Emotion Recognition. ICASSP 2024: 1841-1845 - [c215]Yu-Ting Lan, Wei-Bang Jiang, Wei-Long Zheng, Bao-Liang Lu:
CEMOAE: A Dynamic Autoencoder with Masked Channel Modeling for Robust EEG-Based Emotion Recognition. ICASSP 2024: 1871-1875 - [c214]Pengxuan Gao, Tianyu Liu, Jia-Wen Liu, Bao-Liang Lu, Wei-Long Zheng:
Multimodal Multi-View Spectral-Spatial-Temporal Masked Autoencoder for Self-Supervised Emotion Recognition. ICASSP 2024: 1926-1930 - [c213]Wei-Bang Jiang, Li-Ming Zhao, Bao-Liang Lu:
Large Brain Model for Learning Generic Representations with Tremendous EEG Data in BCI. ICLR 2024 - [c212]Tian-Fang Ma, Lu-Yu Liu, Li-Ming Zhao, Dan Peng, Yong Lu, Wei-Long Zheng, Bao-Liang Lu:
Detecting Major Depression Disorder with Multiview Eye Movement Features in a Novel Oil Painting Paradigm. IJCNN 2024: 1-8 - [c211]Wei-Bang Jiang, Yu-Ting Lan, Bao-Liang Lu:
REmoNet: Reducing Emotional Label Noise via Multi-regularized Self-supervision. ACM Multimedia 2024: 2204-2213 - [c210]Xuan-Hao Liu, Yan-Kai Liu, Yansen Wang, Kan Ren, Hanwen Shi, Zilong Wang, Dongsheng Li, Bao-Liang Lu, Wei-Long Zheng:
EEG2Video: Towards Decoding Dynamic Visual Perception from EEG Signals. NeurIPS 2024 - [i23]Wei-Bang Jiang, Li-Ming Zhao, Bao-Liang Lu:
Large Brain Model for Learning Generic Representations with Tremendous EEG Data in BCI. CoRR abs/2405.18765 (2024) - [i22]Tian-Hua Li, Tian-Fang Ma, Dan Peng, Wei-Long Zheng, Bao-Liang Lu:
Focused State Recognition Using EEG with Eye Movement-Assisted Annotation. CoRR abs/2407.09508 (2024) - [i21]Yifei Yang, Runhan Shi, Zuchao Li, Shu Jiang, Bao-Liang Lu, Yang Yang, Hai Zhao:
BatGPT-Chem: A Foundation Large Model For Retrosynthesis Prediction. CoRR abs/2408.10285 (2024) - [i20]Wei-Bang Jiang, Yansen Wang, Bao-Liang Lu, Dongsheng Li:
NeuroLM: A Universal Multi-task Foundation Model for Bridging the Gap between Language and EEG Signals. CoRR abs/2409.00101 (2024) - [i19]Xuan-Hao Liu, Xinhao Song, Dexuan He, Bao-Liang Lu, Wei-Long Zheng:
Professor X: Manipulating EEG BCI with Invisible and Robust Backdoor Attack. CoRR abs/2409.20158 (2024) - 2023
- [j70]Dongrui Wu, Bao-Liang Lu, Bin Hu, Zhigang Zeng:
Affective Brain-Computer Interfaces (aBCIs): A Tutorial. Proc. IEEE 111(10): 1314-1332 (2023) - [j69]Yong Peng, Keding Chen, Feiping Nie, Bao-Liang Lu, Wanzeng Kong:
Two-Dimensional Embedded Fuzzy Data Clustering. IEEE Trans. Emerg. Top. Comput. Intell. 7(4): 1263-1275 (2023) - [j68]Yong Peng, Wenna Huang, Wanzeng Kong, Feiping Nie, Bao-Liang Lu:
JGSED: An End-to-End Spectral Clustering Model for Joint Graph Construction, Spectral Embedding and Discretization. IEEE Trans. Emerg. Top. Comput. Intell. 7(6): 1687-1701 (2023) - [j67]Yong Peng, Honggang Liu, Wanzeng Kong, Feiping Nie, Bao-Liang Lu, Andrzej Cichocki:
Joint EEG Feature Transfer and Semisupervised Cross-Subject Emotion Recognition. IEEE Trans. Ind. Informatics 19(7): 8104-8115 (2023) - [c209]Jing-Yi Liu, Jia-Wen Liu, Wei-Long Zheng, Bao-Liang Lu:
Transformer-Based Domain Adaptation for Multi-Modal Emotion Recognition in Response to Game Animation Videos. BIBM 2023: 879-884 - [c208]Rong-Fei Gu, Li-Ming Zhao, Wei-Long Zheng, Bao-Liang Lu:
Tagging Continuous Labels for EEG-based Emotion Classification. EMBC 2023: 1-4 - [c207]Yu-Ting Lan, Dan Peng, Wei Liu, Yun Luo, Ziyu Mao, Wei-Long Zheng, Bao-Liang Lu:
Investigating Emotion EEG Patterns for Depression Detection with Attentive Simple Graph Convolutional Network. EMBC 2023: 1-4 - [c206]Luyu Liu, Dan Peng, Wei-Long Zheng, Bao-Liang Lu:
Objective Depression Detection Using EEG and Eye Movement Signals Induced by Oil Paintings. EMBC 2023: 1-4 - [c205]Dan Peng, Wei Liu, Yun Luo, Ziyu Mao, Wei-Long Zheng, Bao-Liang Lu:
Deep Depression Detection with Resting-State and Cognitive-Task EEG. EMBC 2023: 1-4 - [c204]Wei-Bang Jiang, Xu Yan, Wei-Long Zheng, Bao-Liang Lu:
Elastic Graph Transformer Networks for EEG-Based Emotion Recognition. ICASSP 2023: 1-5 - [c203]Xuan-Hao Liu, Wei-Bang Jiang, Wei-Long Zheng, Bao-Liang Lu:
Two-Stream Spectral-Temporal Denoising Network for End-to-End Robust EEG-Based Emotion Recognition. ICONIP (3) 2023: 186-197 - [c202]Jian-Ming Zhang, Jiawen Liu, Ziyi Li, Tian-Fang Ma, Yiting Wang, Wei-Long Zheng, Bao-Liang Lu:
Naturalistic Emotion Recognition Using EEG and Eye Movements. ICONIP (3) 2023: 265-276 - [c201]Zhong-Wei Jin, Jiawen Liu, Wei-Long Zheng, Bao-Liang Lu:
DAformer: Transformer with Domain Adversarial Adaptation for EEG-Based Emotion Recognition with Live-Oil Paintings. ICONIP (9) 2023: 402-414 - [c200]Rong-Fei Gu, Rui Li, Wei-Long Zheng, Bao-Liang Lu:
Cross-Subject Decision Confidence Estimation from EEG Signals Using Spectral-Spatial-Temporal Adaptive GCN with Domain Adaptation. IJCNN 2023: 1-8 - [c199]Wei-Bang Jiang, Xuan-Hao Liu, Wei-Long Zheng, Bao-Liang Lu:
Multimodal Adaptive Emotion Transformer with Flexible Modality Inputs on A Novel Dataset with Continuous Labels. ACM Multimedia 2023: 5975-5984 - [c198]Cheng Fei, Rui Li, Li-Ming Zhao, Wei-Long Zheng, Bao-Liang Lu:
EEG-Eye Movements Cross-Modal Decision Confidence Measurement with Generative Adversarial Networks. NER 2023: 1-4 - [c197]Yu-Ting Lan, Ze-Chen Li, Dan Peng, Wei-Long Zheng, Bao-Liang Lu:
Identifying Artistic Expertise Difference in Emotion Recognition in Response to Oil Paintings. NER 2023: 1-4 - [i18]Yu-Ting Lan, Kan Ren, Yansen Wang, Wei-Long Zheng, Dongsheng Li, Bao-Liang Lu, Lili Qiu:
Seeing through the Brain: Image Reconstruction of Visual Perception from Human Brain Signals. CoRR abs/2308.02510 (2023) - 2022
- [j66]Yixin Wang, Shuang Qiu, Dan Li, Changde Du, Bao-Liang Lu, Huiguang He:
Multi-Modal Domain Adaptation Variational Autoencoder for EEG-Based Emotion Recognition. IEEE CAA J. Autom. Sinica 9(9): 1612-1626 (2022) - [j65]Fangyao Shen, Yong Peng, Guojun Dai, Bao-Liang Lu, Wanzeng Kong:
Coupled Projection Transfer Metric Learning for Cross-Session Emotion Recognition from EEG. Syst. 10(2): 47 (2022) - [j64]Yong Peng, Wenjuan Wang, Wanzeng Kong, Feiping Nie, Bao-Liang Lu, Andrzej Cichocki:
Joint Feature Adaptation and Graph Adaptive Label Propagation for Cross-Subject Emotion Recognition From EEG Signals. IEEE Trans. Affect. Comput. 13(4): 1941-1958 (2022) - [j63]Shu Jiang, Zuchao Li, Hai Zhao, Bao-Liang Lu, Rui Wang:
Tri-training for Dependency Parsing Domain Adaptation. ACM Trans. Asian Low Resour. Lang. Inf. Process. 21(3): 48:1-48:17 (2022) - [j62]Dongrui Wu, Yifan Xu, Bao-Liang Lu:
Transfer Learning for EEG-Based Brain-Computer Interfaces: A Review of Progress Made Since 2016. IEEE Trans. Cogn. Dev. Syst. 14(1): 4-19 (2022) - [j61]Wei Liu, Jie-Lin Qiu, Wei-Long Zheng, Bao-Liang Lu:
Comparing Recognition Performance and Robustness of Multimodal Deep Learning Models for Multimodal Emotion Recognition. IEEE Trans. Cogn. Dev. Syst. 14(2): 715-729 (2022) - [j60]Xing Li, Fangyao Shen, Yong Peng, Wanzeng Kong, Bao-Liang Lu:
Efficient Sample and Feature Importance Mining in Semi-Supervised EEG Emotion Recognition. IEEE Trans. Circuits Syst. II Express Briefs 69(7): 3349-3353 (2022) - [j59]Wei Wu, Wei Sun, Q. M. Jonathan Wu, Yimin Yang, Hui Zhang, Wei-Long Zheng, Bao-Liang Lu:
Multimodal Vigilance Estimation Using Deep Learning. IEEE Trans. Cybern. 52(5): 3097-3110 (2022) - [j58]Yong Peng, Yikai Zhang, Wanzeng Kong, Feiping Nie, Bao-Liang Lu, Andrzej Cichocki:
S3LRR: A Unified Model for Joint Discriminative Subspace Identification and Semisupervised EEG Emotion Recognition. IEEE Trans. Instrum. Meas. 71: 1-13 (2022) - [j57]Yikai Zhang, Ruiqi Guo, Yong Peng, Wanzeng Kong, Feiping Nie, Bao-Liang Lu:
An Auto-Weighting Incremental Random Vector Functional Link Network for EEG-Based Driving Fatigue Detection. IEEE Trans. Instrum. Meas. 71: 1-14 (2022) - [c196]Cheng Fei, Rui Li, Li-Ming Zhao, Ziyi Li, Bao-Liang Lu:
A Cross-modality Deep Learning Method for Measuring Decision Confidence from Eye Movement Signals. EMBC 2022: 3342-3345 - [c195]Shuai Luo, Yu-Ting Lan, Dan Peng, Ziyi Li, Wei-Long Zheng, Bao-Liang Lu:
Multimodal Emotion Recognition in Response to Oil Paintings. EMBC 2022: 4167-4170 - [c194]Ziyi Li, Luyu Liu, Yihui Zhu, Bao-Liang Lu:
Exploring Sex Differences in Key Frequency Bands and Channel Connections for EEG-based Emotion Recognition. EMBC 2022: 4793-4796 - [c193]Rui Li, Yiting Wang, Bao-Liang Lu:
Measuring Decision Confidence Levels from EEG Using a Spectral-Spatial-Temporal Adaptive Graph Convolutional Neural Network. ICONIP (5) 2022: 395-406 - [c192]Tian-Fang Ma, Wei-Long Zheng, Bao-Liang Lu:
Few-Shot Class-Incremental Learning for EEG-Based Emotion Recognition. ICONIP (5) 2022: 445-455 - [c191]Yan-Kai Liu, Wei-Bang Jiang, Bao-Liang Lu:
Increasing the Stability of EEG-based Emotion Recognition with a Variant of Neural Processes. IJCNN 2022: 1-6 - [c190]Rui Li, Yiting Wang, Wei-Long Zheng, Bao-Liang Lu:
A Multi-view Spectral-Spatial-Temporal Masked Autoencoder for Decoding Emotions with Self-supervised Learning. ACM Multimedia 2022: 6-14 - 2021
- [j56]Shu Jiang, Zhuosheng Zhang, Hai Zhao, Jiangtong Li, Yang Yang, Bao-Liang Lu, Ning Xia:
When SMILES Smiles, Practicality Judgment and Yield Prediction of Chemical Reaction via Deep Chemical Language Processing. IEEE Access 9: 85071-85083 (2021) - [j55]Shu Jiang, Rui Wang, Zuchao Li, Masao Utiyama, Kehai Chen, Eiichiro Sumita, Hai Zhao, Bao-Liang Lu:
Document-Level Neural Machine Translation with Associated Memory Network. IEICE Trans. Inf. Syst. 104-D(10): 1712-1723 (2021) - [j54]Wei Wu, Q. M. Jonathan Wu, Wei Sun, Yimin Yang, Xiaofang Yuan, Wei-Long Zheng, Bao-Liang Lu:
A Regression Method With Subnetwork Neurons for Vigilance Estimation Using EOG and EEG. IEEE Trans. Cogn. Dev. Syst. 13(1): 209-222 (2021) - [j53]Wei Wu, Wei Sun, Q. M. Jonathan Wu, Cheng Zhang, Yimin Yang, Hongshan Yu, Bao-Liang Lu:
Faster Single Model Vigilance Detection Based on Deep Learning. IEEE Trans. Cogn. Dev. Syst. 13(3): 621-630 (2021) - [j52]Yong Peng, Wanzeng Kong, Feiwei Qin, Feiping Nie, Jinglong Fang, Bao-Liang Lu, Andrzej Cichocki:
Self-Weighted Semi-Supervised Classification for Joint EEG-Based Emotion Recognition and Affective Activation Patterns Mining. IEEE Trans. Instrum. Meas. 70: 1-11 (2021) - [c189]Li-Ming Zhao, Xu Yan, Bao-Liang Lu:
Plug-and-Play Domain Adaptation for Cross-Subject EEG-based Emotion Recognition. AAAI 2021: 863-870 - [c188]Wei-Bang Jiang, Li-Ming Zhao, Ping Guo, Bao-Liang Lu:
Discriminating Surprise and Anger from EEG and Eye Movements with a Graph Network. BIBM 2021: 1353-1357 - [c187]Yun Luo, Bao-Liang Lu:
Wasserstein-Distance-Based Multi-Source Adversarial Domain Adaptation for Emotion Recognition and Vigilance Estimation. BIBM 2021: 1424-1428 - [c186]Yiting Wang, Wei-Bang Jiang, Rui Li, Bao-Liang Lu:
Emotion Transformer Fusion: Complementary Representation Properties of EEG and Eye Movements on Recognizing Anger and Surprise. BIBM 2021: 1575-1578 - [c185]Hao-Yi Hu, Li-Ming Zhao, Yu-Zhong Liu, Hua-Liang Li, Bao-Liang Lu:
A Novel Experiment Setting for Cross-subject Emotion Recognition. EMBC 2021: 6416-6419 - [c184]Rui-Xiao Ma, Xu Yan, Yu-Zhong Liu, Hua-Liang Li, Bao-Liang Lu:
Sex Difference in Emotion Recognition under Sleep Deprivation: Evidence from EEG and Eye-tracking. EMBC 2021: 6449-6452 - [c183]Jian-Ming Zhang, Xu Yan, Ziyi Li, Li-Ming Zhao, Yu-Zhong Liu, Hua-Liang Li, Bao-Liang Lu:
A Cross-subject and Cross-modal Model for Multimodal Emotion Recognition. ICONIP (6) 2021: 203-211 - [c182]Le-Dian Liu, Rui Li, Yu-Zhong Liu, Hua-Liang Li, Bao-Liang Lu:
EEG-Based Human Decision Confidence Measurement Using Graph Neural Networks. ICONIP (6) 2021: 291-298 - [c181]Xu Yan, Li-Ming Zhao, Bao-Liang Lu:
Simplifying Multimodal Emotion Recognition with Single Eye Movement Modality. ACM Multimedia 2021: 1057-1063 - [c180]Rui Li, Yiting Wang, Bao-Liang Lu:
A Multi-Domain Adaptive Graph Convolutional Network for EEG-based Emotion Recognition. ACM Multimedia 2021: 5565-5573 - [c179]Zhi-Wei Zhao, Wei Liu, Bao-Liang Lu:
Multimodal Emotion Recognition Using a Modified Dense Co-Attention Symmetric Network. NER 2021: 73-76 - [c178]Rui Li, Le-Dian Liu, Bao-Liang Lu:
Measuring Human Decision Confidence from EEG Signals in an Object Detection Task. NER 2021: 942-945 - [c177]Rui Li, Le-Dian Liu, Bao-Liang Lu:
Discrimination of Decision Confidence Levels from EEG Signals. NER 2021: 946-949 - 2020
- [j51]Yingying Jiao, Yini Deng, Yun Luo, Bao-Liang Lu:
Driver sleepiness detection from EEG and EOG signals using GAN and LSTM networks. Neurocomputing 408: 100-111 (2020) - [j50]Wei-Long Zheng, Kunpeng Gao, Gang Li, Wei Liu, Chao Liu, Jing-Quan Liu, Guoxing Wang, Bao-Liang Lu:
Vigilance Estimation Using a Wearable EOG Device in Real Driving Environment. IEEE Trans. Intell. Transp. Syst. 21(1): 170-184 (2020) - [c176]Chen-Li Yao, Bao-Liang Lu:
A Robust Approach to Estimating Vigilance from EEG with Neural Processes. BIBM 2020: 1202-1205 - [c175]Wenrui Mu, Bao-Liang Lu:
Examining Four Experimental Paradigms for EEG-Based Sleep Quality Evaluation with Domain Adaptation. EMBC 2020: 5913-5916 - [c174]Yong Peng, Qingxi Li, Wanzeng Kong, Jianhai Zhang, Bao-Liang Lu, Andrzej Cichocki:
Joint Semi-Supervised Feature Auto-Weighting and Classification Model for EEG-Based Cross-Subject Sleep Quality Evaluation. ICASSP 2020: 946-950 - [c173]Yu-Ting Lan, Wei Liu, Bao-Liang Lu:
Multimodal Emotion Recognition Using Deep Generalized Canonical Correlation Analysis with an Attention Mechanism. IJCNN 2020: 1-6 - [c172]Le-Yan Tao, Bao-Liang Lu:
Emotion Recognition under Sleep Deprivation Using a Multimodal Residual LSTM Network. IJCNN 2020: 1-8 - [c171]Hao Tang, Zhiao Huang, Jiayuan Gu, Bao-Liang Lu, Hao Su:
Towards Scale-Invariant Graph-related Problem Solving by Iterative Homogeneous GNNs. NeurIPS 2020 - [i17]Xun Wu, Wei-Long Zheng, Bao-Liang Lu:
Investigating EEG-Based Functional Connectivity Patterns for Multimodal Emotion Recognition. CoRR abs/2004.01973 (2020) - [i16]Dongrui Wu, Yifan Xu, Bao-Liang Lu:
Transfer Learning for EEG-Based Brain-Computer Interfaces: A Review of Progresses Since 2016. CoRR abs/2004.06286 (2020) - [i15]Yun Luo, Lizhen Zhu, Zi-Yu Wan, Bao-Liang Lu:
Data Augmentation for Enhancing EEG-based Emotion Recognition with Deep Generative Models. CoRR abs/2006.05331 (2020) - [i14]Shu Jiang, Hai Zhao, Zuchao Li, Bao-Liang Lu:
Document-level Neural Machine Translation with Document Embeddings. CoRR abs/2009.08775 (2020) - [i13]Hao Tang, Zhiao Huang, Jiayuan Gu, Bao-Liang Lu, Hao Su:
Towards Scale-Invariant Graph-related Problem Solving by Iterative Homogeneous Graph Neural Networks. CoRR abs/2010.13547 (2020)
2010 – 2019
- 2019
- [j49]Huangfei Jiang, Xiya Guan, Wei-Ye Zhao, Li-Ming Zhao, Bao-Liang Lu:
Generating Multimodal Features for Emotion Classification from Eye Movement Signals. Aust. J. Intell. Inf. Process. Syst. 15(3): 59-66 (2019) - [j48]Wei-Long Zheng, Jia-Yi Zhu, Bao-Liang Lu:
Identifying Stable Patterns over Time for Emotion Recognition from EEG. IEEE Trans. Affect. Comput. 10(3): 417-429 (2019) - [j47]Wei-Long Zheng, Wei Liu, Yifei Lu, Bao-Liang Lu, Andrzej Cichocki:
EmotionMeter: A Multimodal Framework for Recognizing Human Emotions. IEEE Trans. Cybern. 49(3): 1110-1122 (2019) - [c170]Jiang-Jian Guo, Rong Zhou, Li-Ming Zhao, Bao-Liang Lu:
Multimodal Emotion Recognition from Eye Image, Eye Movement and EEG Using Deep Neural Networks. EMBC 2019: 3071-3074 - [c169]Lan-Qing Bao, Jie-Lin Qiu, Hao Tang, Wei-Long Zheng, Bao-Liang Lu:
Investigating Sex Differences in Classification of Five Emotions from EEG and Eye Movement Signals. EMBC 2019: 6746-6749 - [c168]Bo-Qun Ma, He Li, Wei-Long Zheng, Bao-Liang Lu:
Reducing the Subject Variability of EEG Signals with Adversarial Domain Generalization. ICONIP (1) 2019: 30-42 - [c167]Lu Gan, Wei Liu, Yun Luo, Xun Wu, Bao-Liang Lu:
A Cross-Culture Study on Multimodal Emotion Recognition Using Deep Learning. ICONIP (4) 2019: 670-680 - [c166]Bo-Qun Ma, He Li, Yun Luo, Bao-Liang Lu:
Depersonalized Cross-Subject Vigilance Estimation with Adversarial Domain Generalization. IJCNN 2019: 1-8 - [c165]Yun Luo, Lizhen Zhu, Bao-Liang Lu:
A GAN-Based Data Augmentation Method for Multimodal Emotion Recognition. ISNN (1) 2019: 141-150 - [c164]Jia-Xin Ma, Hao Tang, Wei-Long Zheng, Bao-Liang Lu:
Emotion Recognition using Multimodal Residual LSTM Network. ACM Multimedia 2019: 176-183 - [c163]Xun Wu, Wei-Long Zheng, Bao-Liang Lu:
Identifying Functional Brain Connectivity Patterns for EEG-Based Emotion Recognition. NER 2019: 235-238 - [c162]Tian-Hao Li, Wei Liu, Wei-Long Zheng, Bao-Liang Lu:
Classification of Five Emotions from EEG and Eye Movement Signals: Discrimination Ability and Stability over Time. NER 2019: 607-610 - [c161]Li-Ming Zhao, Rui Li, Wei-Long Zheng, Bao-Liang Lu:
Classification of Five Emotions from EEG and Eye Movement Signals: Complementary Representation Properties. NER 2019: 611-614 - [i12]Shu Jiang, Zhuosheng Zhang, Hai Zhao, Jiangtong Li, Yang Yang, Bao-Liang Lu, Ning Xia:
Judging Chemical Reaction Practicality From Positive Sample only Learning. CoRR abs/1904.09824 (2019) - [i11]Wei Liu, Jie-Lin Qiu, Wei-Long Zheng, Bao-Liang Lu:
Multimodal Emotion Recognition Using Deep Canonical Correlation Analysis. CoRR abs/1908.05349 (2019) - [i10]Shu Jiang, Rui Wang, Zuchao Li, Masao Utiyama, Kehai Chen, Eiichiro Sumita, Hai Zhao, Bao-Liang Lu:
Document-level Neural Machine Translation with Inter-Sentence Attention. CoRR abs/1910.14528 (2019) - 2018
- [j46]Rui Wang, Hai Zhao, Sabine Ploux, Bao-Liang Lu, Masao Utiyama, Eiichiro Sumita:
Graph-Based Bilingual Word Embedding for Statistical Machine Translation. ACM Trans. Asian Low Resour. Lang. Inf. Process. 17(4): 31:1-31:23 (2018) - [j45]Yimin Yang, Q. M. Jonathan Wu, Wei-Long Zheng, Bao-Liang Lu:
EEG-Based Emotion Recognition Using Hierarchical Network With Subnetwork Nodes. IEEE Trans. Cogn. Dev. Syst. 10(2): 408-419 (2018) - [c160]Jie-Lin Qiu, Wei Liu, Bao-Liang Lu:
Multi-view Emotion Recognition Using Deep Canonical Correlation Analysis. ICONIP (5) 2018: 221-231 - [c159]Yun Luo, Si-Yang Zhang, Wei-Long Zheng, Bao-Liang Lu:
WGAN Domain Adaptation for EEG-Based Emotion Recognition. ICONIP (5) 2018: 275-286 - [c158]Li-Ming Zhao, Xin-Wei Li, Wei-Long Zheng, Bao-Liang Lu:
Active Feedback Framework with Scan-Path Clustering for Deep Affective Models. ICONIP (2) 2018: 330-340 - [c157]He Li, Yi-Ming Jin, Wei-Long Zheng, Bao-Liang Lu:
Cross-Subject Emotion Recognition Using Deep Adaptation Networks. ICONIP (5) 2018: 403-413 - [c156]Yini Deng, Yingying Jiao, Bao-Liang Lu:
Driver Sleepiness Detection Using LSTM Neural Network. ICONIP (4) 2018: 622-633 - [c155]He Li, Wei-Long Zheng, Bao-Liang Lu:
Multimodal Vigilance Estimation with Adversarial Domain Adaptation Networks. IJCNN 2018: 1-6 - [c154]Jia-Jun Tong, Yun Luo, Bo-Qun Ma, Wei-Long Zheng, Bao-Liang Lu, Xiao-Qi Song, Shi-Wei Ma:
Sleep Quality Estimation with Adversarial Domain Adaptation: From Laboratory to Real Scenario. IJCNN 2018: 1-8 - [c153]Changde Du, Changying Du, Hao Wang, Jinpeng Li, Wei-Long Zheng, Bao-Liang Lu, Huiguang He:
Semi-supervised Deep Generative Modelling of Incomplete Multi-Modality Emotional Data. ACM Multimedia 2018: 108-116 - [i9]Changde Du, Changying Du, Hao Wang, Jinpeng Li, Wei-Long Zheng, Bao-Liang Lu, Huiguang He:
Semi-supervised Deep Generative Modelling of Incomplete Multi-Modality Emotional Data. CoRR abs/1808.02096 (2018) - 2017
- [j44]Yong Peng, Bao-Liang Lu:
Discriminative extreme learning machine with supervised sparsity preserving for image classification. Neurocomputing 261: 242-252 (2017) - [j43]Kai-Ming Jiang, Ya-Jing Chen, Jin-Xiong Lv, Bao-Liang Lu, Lei Xu:
Bootstrapping integrative hypothesis test for identifying biomarkers that differentiates lung cancer and chronic obstructive pulmonary disease. Neurocomputing 269: 40-46 (2017) - [j42]Yong Peng, Bao-Liang Lu:
Robust structured sparse representation via half-quadratic optimization for face recognition. Multim. Tools Appl. 76(6): 8859-8880 (2017) - [j41]Wei-Long Zheng, Shan-Chun Shen, Bao-Liang Lu:
Online Depth Image-Based Object Tracking with Sparse Representation and Object Detection. Neural Process. Lett. 45(3): 745-758 (2017) - [c152]Yingying Jiao, Bao-Liang Lu:
Detecting driver sleepiness from EEG alpha wave during daytime driving. BIBM 2017: 728-731 - [c151]Xue Yan, Wei-Long Zheng, Wei Liu, Bao-Liang Lu:
Identifying Gender Differences in Multimodal Emotion Recognition Using Bimodal Deep AutoEncoder. ICONIP (4) 2017: 533-542 - [c150]Xing-Zan Zhang, Wei-Long Zheng, Bao-Liang Lu:
EEG-Based Sleep Quality Evaluation with Deep Transfer Learning. ICONIP (4) 2017: 543-552 - [c149]Wei-Ye Zhao, Sheng Fang, Ting Ji, Qian Ji, Wei-Long Zheng, Bao-Liang Lu:
Emotion Annotation Using Hierarchical Aligned Cluster Analysis. ICONIP (4) 2017: 572-580 - [c148]Hao Tang, Wei Liu, Wei-Long Zheng, Bao-Liang Lu:
Multimodal Emotion Recognition Using Deep Neural Networks. ICONIP (4) 2017: 811-819 - [c147]Xue Yan, Wei-Long Zheng, Wei Liu, Bao-Liang Lu:
Investigating Gender Differences of Brain Areas in Emotion Recognition Using LSTM Neural Network. ICONIP (4) 2017: 820-829 - [c146]Yingying Jiao, Bao-Liang Lu:
An alpha wave pattern from attenuation to disappearance for predicting the entry into sleep during simulated driving. NER 2017: 21-24 - [c145]Li-Huan Du, Wei Liu, Wei-Long Zheng, Bao-Liang Lu:
Detecting driving fatigue with multimodal deep learning. NER 2017: 74-77 - [c144]Zhen-Feng Shi, Chang Zhou, Wei-Long Zheng, Bao-Liang Lu:
Attention evaluation with eye tracking glasses for EEG-based emotion recognition. NER 2017: 86-89 - [c143]Si-Yuan Wu, Moritz Schaefer, Wei-Long Zheng, Bao-Liang Lu, Hiroshi Yokoi:
Neural patterns between Chinese and Germans for EEG-based emotion recognition. NER 2017: 94-97 - [i8]Changde Du, Changying Du, Jinpeng Li, Wei-Long Zheng, Bao-Liang Lu, Huiguang He:
Semi-supervised Bayesian Deep Multi-modal Emotion Recognition. CoRR abs/1704.07548 (2017) - 2016
- [j40]Yong Peng, Wei-Long Zheng, Bao-Liang Lu:
An unsupervised discriminative extreme learning machine and its applications to data clustering. Neurocomputing 174: 250-264 (2016) - [j39]Yong Peng, Bao-Liang Lu:
Discriminative manifold extreme learning machine and applications to image and EEG signal classification. Neurocomputing 174: 265-277 (2016) - [j38]Rui Wang, Masao Utiyama, Isao Goto, Eiichiro Sumita, Hai Zhao, Bao-Liang Lu:
Converting Continuous-Space Language Models into N-gram Language Models with Efficient Bilingual Pruning for Statistical Machine Translation. ACM Trans. Asian Low Resour. Lang. Inf. Process. 15(3): 11:1-11:26 (2016) - [c142]Jincheng Mei, Hao Zhang, Bao-Liang Lu:
On the Reducibility of Submodular Functions. AISTATS 2016: 186-194 - [c141]Rui Wang, Hai Zhao, Bao-Liang Lu, Masao Utiyama, Eiichiro Sumita:
Connecting Phrase based Statistical Machine Translation Adaptation. COLING 2016: 3135-3145 - [c140]Yingying Jiao, Bao-Liang Lu:
Detecting slow eye movement for recognizing driver's sleep onset period with EEG features. EMBC 2016: 4658-4661 - [c139]Wei Liu, Wei-Long Zheng, Bao-Liang Lu:
Emotion Recognition Using Multimodal Deep Learning. ICONIP (2) 2016: 521-529 - [c138]Nan Zhang, Wei-Long Zheng, Wei Liu, Bao-Liang Lu:
Continuous Vigilance Estimation Using LSTM Neural Networks. ICONIP (2) 2016: 530-537 - [c137]Wei-Long Zheng, Bao-Liang Lu:
Personalizing EEG-Based Affective Models with Transfer Learning. IJCAI 2016: 2732-2739 - [c136]Rui Wang, Hai Zhao, Sabine Ploux, Bao-Liang Lu, Masao Utiyama:
A Bilingual Graph-Based Semantic Model for Statistical Machine Translation. IJCAI 2016: 2950-2956 - [c135]Xue-Qin Huo, Wei-Long Zheng, Bao-Liang Lu:
Driving fatigue detection with fusion of EEG and forehead EOG. IJCNN 2016: 897-904 - [c134]Li-Li Wang, Wei-Long Zheng, Hai-Wei Ma, Bao-Liang Lu:
Measuring sleep quality from EEG with machine learning approaches. IJCNN 2016: 905-912 - [i7]Jincheng Mei, Hao Zhang, Bao-Liang Lu:
On the Reducibility of Submodular Functions. CoRR abs/1601.00393 (2016) - [i6]Wei-Long Zheng, Jia-Yi Zhu, Bao-Liang Lu:
Identifying Stable Patterns over Time for Emotion Recognition from EEG. CoRR abs/1601.02197 (2016) - [i5]Wei Liu, Wei-Long Zheng, Bao-Liang Lu:
Multimodal Emotion Recognition Using Multimodal Deep Learning. CoRR abs/1602.08225 (2016) - [i4]Rui Wang, Hai Zhao, Sabine Ploux, Bao-Liang Lu, Masao Utiyama:
A Novel Bilingual Word Embedding Method for Lexical Translation Using Bilingual Sense Clique. CoRR abs/1607.08692 (2016) - [i3]Rui Wang, Hai Zhao, Bao-Liang Lu, Masao Utiyama, Eiichiro Sumita:
Connecting Phrase based Statistical Machine Translation Adaptation. CoRR abs/1607.08693 (2016) - [i2]Wei-Long Zheng, Bao-Liang Lu:
A Multimodal Approach to Estimating Vigilance Using EEG and Forehead EOG. CoRR abs/1611.08492 (2016) - 2015
- [j37]Yong Peng, Suhang Wang, Xianzhong Long, Bao-Liang Lu:
Discriminative graph regularized extreme learning machine and its application to face recognition. Neurocomputing 149: 340-353 (2015) - [j36]Yong Peng, Bao-Liang Lu:
Hybrid learning clonal selection algorithm. Inf. Sci. 296: 128-146 (2015) - [j35]Yong Peng, Bao-Liang Lu, Suhang Wang:
Enhanced low-rank representation via sparse manifold adaption for semi-supervised learning. Neural Networks 65: 1-17 (2015) - [j34]Yong Peng, Xianzhong Long, Bao-Liang Lu:
Graph Based Semi-Supervised Learning via Structure Preserving Low-Rank Representation. Neural Process. Lett. 41(3): 389-406 (2015) - [j33]Wei-Long Zheng, Bao-Liang Lu:
Investigating Critical Frequency Bands and Channels for EEG-Based Emotion Recognition with Deep Neural Networks. IEEE Trans. Auton. Ment. Dev. 7(3): 162-175 (2015) - [j32]Rui Wang, Hai Zhao, Bao-Liang Lu, Masao Utiyama, Eiichiro Sumita:
Bilingual Continuous-Space Language Model Growing for Statistical Machine Translation. IEEE ACM Trans. Audio Speech Lang. Process. 23(7): 1209-1220 (2015) - [j31]Mu Li, Wei Bi, James T. Kwok, Bao-Liang Lu:
Large-Scale Nyström Kernel Matrix Approximation Using Randomized SVD. IEEE Trans. Neural Networks Learn. Syst. 26(1): 152-164 (2015) - [c133]Jincheng Mei, Kang Zhao, Bao-Liang Lu:
On Unconstrained Quasi-Submodular Function Optimization. AAAI 2015: 1191-1197 - [c132]Wei-Long Zheng, Yong-Qi Zhang, Jia-Yi Zhu, Bao-Liang Lu:
Transfer components between subjects for EEG-based emotion recognition. ACII 2015: 917-922 - [c131]Yong Peng, Bao-Liang Lu:
Robust group sparse representation via half-quadratic optimization for face recognition. CCECE 2015: 146-151 - [c130]Yangcheng He, Hongtao Lu, Bao-Liang Lu:
Graph regularized non-negative local coordinate factorization with pairwise constraints for image representation. ICME 2015: 1-6 - [c129]Li Wu, Kang Zhao, Hongtao Lu, Zhen Wei, Bao-Liang Lu:
Distance Preserving Marginal Hashing for image retrieval. ICME 2015: 1-6 - [c128]Yong-Qi Zhang, Wei-Long Zheng, Bao-Liang Lu:
Transfer Components Between Subjects for EEG-based Driving Fatigue Detection. ICONIP (4) 2015: 61-68 - [c127]Yang Cao, Bao-Liang Lu:
Intensity-Depth Face Alignment Using Cascade Shape Regression. ICONIP (4) 2015: 224-231 - [c126]Yifei Lu, Wei-Long Zheng, Binbin Li, Bao-Liang Lu:
Combining Eye Movements and EEG to Enhance Emotion Recognition. IJCAI 2015: 1170-1176 - [c125]Kai-Ming Jiang, Bao-Liang Lu, Lei Xu:
Bootstrapped Integrative Hypothesis Test, COPD-Lung Cancer Differentiation, and Joint miRNAs Biomarkers. IScIDE (2) 2015: 538-547 - [c124]Wei-Long Zheng, Hao-Tian Guo, Bao-Liang Lu:
Revealing critical channels and frequency bands for emotion recognition from EEG with deep belief network. NER 2015: 154-157 - [c123]Yu-Fei Zhang, Xiang-Yu Gao, Jia-Yi Zhu, Wei-Long Zheng, Bao-Liang Lu:
A novel approach to driving fatigue detection using forehead EOG. NER 2015: 707-710 - [c122]Xiang-Yu Gao, Yu-Fei Zhang, Wei-Long Zheng, Bao-Liang Lu:
Evaluating driving fatigue detection algorithms using eye tracking glasses. NER 2015: 767-770 - [c121]Rui Wang, Hai Zhao, Bao-Liang Lu:
English to Chinese Translation: How Chinese Character Matters. PACLIC 2015 - 2014
- [j30]Xiaolin Wang, Yangyang Chen, Hai Zhao, Bao-Liang Lu:
Parallelized extreme learning machine ensemble based on min-max modular network. Neurocomputing 128: 31-41 (2014) - [j29]Xiao-Wei Wang, Dan Nie, Bao-Liang Lu:
Emotional state classification from EEG data using machine learning approach. Neurocomputing 129: 94-106 (2014) - [j28]Jing-Nan Gu, Hong-Tao Lu, Bao-Liang Lu:
An integrated Gaussian mixture model to estimate vigilance level based on EEG recordings. Neurocomputing 129: 107-113 (2014) - [j27]Xiaolin Wang, Hai Zhao, Bao-Liang Lu:
A Meta-Top-Down Method for Large-Scale Hierarchical Classification. IEEE Trans. Knowl. Data Eng. 26(3): 500-513 (2014) - [c120]Yong Peng, Jia-Yi Zhu, Wei-Long Zheng, Bao-Liang Lu:
EEG-based emotion recognition with manifold regularized extreme learning machine. EMBC 2014: 974-977 - [c119]Wei-Long Zheng, Bo-Nan Dong, Bao-Liang Lu:
Multimodal emotion recognition using EEG and eye tracking data. EMBC 2014: 5040-5043 - [c118]Rui Wang, Hai Zhao, Bao-Liang Lu, Masao Utiyama, Eiichiro Sumita:
Neural Network Based Bilingual Language Model Growing for Statistical Machine Translation. EMNLP 2014: 189-195 - [c117]Wei-Long Zheng, Jia-Yi Zhu, Bao-Liang Lu:
Multimodel emotion analysis in response to multimedia. ICME Workshops 2014: 1-2 - [c116]Wei-Long Zheng, Jia-Yi Zhu, Yong Peng, Bao-Liang Lu:
EEG-based emotion classification using deep belief networks. ICME 2014: 1-6 - [c115]Jincheng Mei, Bao-Liang Lu:
Saliency Level Set Evolution. ICONIP (2) 2014: 170-177 - [c114]Shan-Chun Shen, Wei-Long Zheng, Bao-Liang Lu:
Online Object Tracking Based on Depth Image with Sparse Coding. ICONIP (3) 2014: 234-241 - [c113]Xuemin Zhu, Wei-Long Zheng, Bao-Liang Lu, Xiaoping Chen, Shanguang Chen, Chunhui Wang:
EOG-based drowsiness detection using convolutional neural networks. IJCNN 2014: 128-134 - [c112]Jia-Yi Zhu, Wei-Long Zheng, Yong Peng, Ruo-Nan Duan, Bao-Liang Lu:
EEG-based emotion recognition using discriminative graph regularized extreme learning machine. IJCNN 2014: 525-532 - [c111]Yingying Jiao, Yong Peng, Bao-Liang Lu, Xiaoping Chen, Shanguang Chen, Chunhui Wang:
Recognizing slow eye movement for driver fatigue detection with machine learning approach. IJCNN 2014: 4035-4041 - [i1]Jincheng Mei, Kang Zhao, Bao-Liang Lu:
Unconstrained Quasi-Submodular Function Optimization. CoRR abs/1408.4389 (2014) - 2013
- [j26]Yong Peng, Bao-Liang Lu:
A hierarchical particle swarm optimizer with latin sampling based memetic algorithm for numerical optimization. Appl. Soft Comput. 13(5): 2823-2836 (2013) - [j25]Li-Chen Shi, Bao-Liang Lu:
EEG-based vigilance estimation using extreme learning machines. Neurocomputing 102: 135-143 (2013) - [c110]Hai Zhao, Masao Utiyama, Eiichiro Sumita, Bao-Liang Lu:
An Empirical Study on Word Segmentation for Chinese Machine Translation. CICLing (2) 2013: 248-263 - [c109]Li-Chen Shi, Ruo-Nan Duan, Bao-Liang Lu:
A robust principal component analysis algorithm for EEG-based vigilance estimation. EMBC 2013: 6623-6626 - [c108]Li-Chen Shi, Yingying Jiao, Bao-Liang Lu:
Differential entropy feature for EEG-based vigilance estimation. EMBC 2013: 6627-6630 - [c107]Rui Wang, Masao Utiyama, Isao Goto, Eiichiro Sumita, Hai Zhao, Bao-Liang Lu:
Converting Continuous-Space Language Models into N-Gram Language Models for Statistical Machine Translation. EMNLP 2013: 845-850 - [c106]Fan Li, Xiao-Wei Wang, Bao-Liang Lu:
Detection of Driving Fatigue Based on Grip Force on Steering Wheel with Wavelet Transformation and Support Vector Machine. ICONIP (3) 2013: 141-148 - [c105]Yong Peng, Suhang Wang, Shen Wang, Bao-Liang Lu:
Structure Preserving Low-Rank Representation for Semi-supervised Face Recognition. ICONIP (2) 2013: 148-155 - [c104]Yong Peng, Shen Wang, Bao-Liang Lu:
Marginalized Denoising Autoencoder via Graph Regularization for Domain Adaptation. ICONIP (2) 2013: 156-163 - [c103]Yang Cao, Bao-Liang Lu:
Real-Time Head Detection with Kinect for Driving Fatigue Detection. ICONIP (3) 2013: 600-607 - [c102]Xiaolin Wang, Hai Zhao, Bao-Liang Lu:
Labeled Alignment for Recognizing Textual Entailment. IJCNLP 2013: 605-613 - [c101]Xiaolin Wang, Hai Zhao, Bao-Liang Lu:
BCMI-NLP Labeled-Alignment-Based Entailment System for NTCIR-10 RITE-2 Task. NTCIR 2013 - 2012
- [j24]Yen-Wei Chen, Ikuko Nishikawa, Shinichi Tamura, Bao-Liang Lu, Huiyan Jiang:
Computational Intelligence in Biomedical Science and Engineering. Comput. Intell. Neurosci. 2012: 160356:1-160356:2 (2012) - [j23]Bing Li, Xiao-Chen Lian, Bao-Liang Lu:
Gender classification by combining clothing, hair and facial component classifiers. Neurocomputing 76(1): 18-27 (2012) - [j22]Tian-Xiang Wu, Xiao-Chen Lian, Bao-Liang Lu:
Multi-view gender classification using symmetry of facial images. Neural Comput. Appl. 21(4): 661-669 (2012) - [c100]Xiaolin Wang, Hai Zhao, Bao-Liang Lu:
Automated quality assessment of web pages from textual content. ICMLC 2012: 2000-2006 - [c99]Ruofei Du, Ren-Jie Liu, Tian-Xiang Wu, Bao-Liang Lu:
Online Vigilance Analysis Combining Video and Electrooculography Features. ICONIP (5) 2012: 447-454 - [c98]Ruo-Nan Duan, Xiao-Wei Wang, Bao-Liang Lu:
EEG-Based Emotion Recognition in Listening Music by Using Support Vector Machine and Linear Dynamic System. ICONIP (4) 2012: 468-475 - [c97]Hui Sun, Bao-Liang Lu:
EEG-Based Fatigue Classification by Using Parallel Hidden Markov Model and Pattern Classifier Combination. ICONIP (4) 2012: 484-491 - [c96]Yangyang Chen, Bao-Liang Lu, Hai Zhao:
Parallel learning of large-scale multi-label classification problems with min-max modular LIBLINEAR. IJCNN 2012: 1-7 - [c95]Zheng-Ping Wei, Bao-Liang Lu:
Online vigilance analysis based on electrooculography. IJCNN 2012: 1-7 - [c94]Yong Peng, Bao-Liang Lu:
Immune clonal algorithm based feature selection for epileptic EEG signal classification. ISSPA 2012: 848-853 - [c93]Shaohua Yang, Hai Zhao, Xiaolin Wang, Bao-Liang Lu:
Spell Checking for Chinese. LREC 2012: 730-736 - [c92]Shaohua Yang, Hai Zhao, Bao-Liang Lu:
Towards a Semantic Annotation of English Television News - Building and Evaluating a Constraint Grammar FrameNet. PACLIC 2012: 333-342 - 2011
- [j21]Jian Zhang, Hai Zhao, Liqing Zhang, Bao-Liang Lu:
An Empirical Comparative Study on Two Large-Scale Hierarchical Text Classification Approaches. Int. J. Comput. Process. Orient. Lang. 23(4): 309-326 (2011) - [j20]Ji Zheng, Bao-Liang Lu:
A support vector machine classifier with automatic confidence and its application to gender classification. Neurocomputing 74(11): 1926-1935 (2011) - [j19]Wen-Yun Yang, Bao-Liang Lu, James T. Kwok:
Incorporating cellular sorting structure for better prediction of protein subcellular locations. J. Exp. Theor. Artif. Intell. 23(1): 79-95 (2011) - [c91]Bing Li, Rong Xiao, Zhiwei Li, Rui Cai, Bao-Liang Lu, Lei Zhang:
Rank-SIFT: Learning to rank repeatable local interest points. CVPR 2011: 1737-1744 - [c90]Mu Li, Xiao-Chen Lian, James T. Kwok, Bao-Liang Lu:
Time and space efficient spectral clustering via column sampling. CVPR 2011: 2297-2304 - [c89]Hao-Yu Cai, Jia-Xin Ma, Li-Chen Shi, Bao-Liang Lu:
A novel method for EOG features extraction from the forehead. EMBC 2011: 3075-3078 - [c88]Zhong-Lei Gu, Li-Chen Shi, Bao-Liang Lu:
Evidence of rapid gender processing revealed by ERSP. EMBC 2011: 7360-7363 - [c87]Jing-Nan Gu, Hong-Jun Liu, Hong-Tao Lu, Bao-Liang Lu:
An Integrated Hierarchical Gaussian Mixture Model to Estimate Vigilance Level Based on EEG Recordings. ICONIP (1) 2011: 380-387 - [c86]Jie Wu, Li-Chen Shi, Bao-Liang Lu:
Removing Unrelated Features Based on Linear Dynamical System for Motor-Imagery-Based Brain-Computer Interface. ICONIP (1) 2011: 709-716 - [c85]Li-Chen Shi, Yang Li, Rui-Hua Sun, Bao-Liang Lu:
A Sparse Common Spatial Pattern Algorithm for Brain-Computer Interface. ICONIP (1) 2011: 725-733 - [c84]Xiao-Wei Wang, Dan Nie, Bao-Liang Lu:
EEG-Based Emotion Recognition Using Frequency Domain Features and Support Vector Machines. ICONIP (1) 2011: 734-743 - [c83]Xiaolin Wang, Hai Zhao, Bao-Liang Lu:
Enhance Top-down method with Meta-Classification for Very Large-scale Hierarchical Classification. IJCNLP 2011: 1089-1097 - [c82]Xiaolin Wang, Hai Zhao, Bao-Liang Lu:
GeoTime Retrieval through Passage-based Learning to Rank. NTCIR 2011 - [c81]Xiaolin Wang, Hai Zhao, Bao-Liang Lu:
Redundancy Removal to Selectively Diversify Information Retrieval Results. NTCIR 2011 - [e8]Bao-Liang Lu, Liqing Zhang, James T. Kwok:
Neural Information Processing - 18th International Conference, ICONIP 2011, Shanghai, China, November 13-17, 2011, Proceedings, Part I. Lecture Notes in Computer Science 7062, Springer 2011, ISBN 978-3-642-24954-9 [contents] - [e7]Bao-Liang Lu, Liqing Zhang, James T. Kwok:
Neural Information Processing - 18th International Conference, ICONIP 2011, Shanghai, China, November 13-17, 2011, Proceedings, Part II. Lecture Notes in Computer Science 7063, Springer 2011, ISBN 978-3-642-24957-0 [contents] - [e6]Bao-Liang Lu, Liqing Zhang, James T. Kwok:
Neural Information Processing - 18th International Conference, ICONIP 2011, Shanghai, China, November 13-17, 2011, Proceedings, Part III. Lecture Notes in Computer Science 7064, Springer 2011, ISBN 978-3-642-24964-8 [contents] - 2010
- [j18]Yang Yang, Bao-Liang Lu:
Protein Subcellular Multi-Localization Prediction Using a Min-Max Modular Support Vector Machine. Int. J. Neural Syst. 20(1): 13-28 (2010) - [j17]Wen Yu, Bao-Liang Lu:
Modeling and Adaptive Control with Fuzzy Neural Networks - Selected Papers from the 6th International Symposium on Neural Networks. Neurocomputing 73(13-15): 2430 (2010) - [j16]Hai Zhao, Changning Huang, Mu Li, Bao-Liang Lu:
A Unified Character-Based Tagging Framework for Chinese Word Segmentation. ACM Trans. Asian Lang. Inf. Process. 9(2): 5:1-5:32 (2010) - [c80]Xuezhe Ma, Xiaotian Zhang, Hai Zhao, Bao-Liang Lu:
Dependency Parser for Chinese Constituent Parsing. CIPS-SIGHAN 2010 - [c79]Shaodian Zhang, Hai Zhao, Guodong Zhou, Bao-Liang Lu:
Hedge Detection and Scope Finding by Sequence Labeling with Procedural Feature Selection. CoNLL Shared Task 2010: 92-99 - [c78]Mu Li, James T. Kwok, Bao-Liang Lu:
Online multiple instance learning with no regret. CVPR 2010: 1395-1401 - [c77]Xiao-Chen Lian, Zhiwei Li, Changhu Wang, Bao-Liang Lu, Lei Zhang:
Probabilistic models for supervised dictionary learning. CVPR 2010: 2305-2312 - [c76]Xiao-Chen Lian, Zhiwei Li, Bao-Liang Lu, Lei Zhang:
Max-Margin Dictionary Learning for Multiclass Image Categorization. ECCV (4) 2010: 157-170 - [c75]Xiaolin Wang, Bao-Liang Lu:
Flatten hierarchies for large-scale hierarchical text categorization. ICDIM 2010: 139-144 - [c74]Tianqi Zhang, Bao-Liang Lu:
Selecting Optimal Orientations of Gabor Wavelet Filters for Facial Image Analysis. ICISP 2010: 218-227 - [c73]Mu Li, James T. Kwok, Bao-Liang Lu:
Making Large-Scale Nyström Approximation Possible. ICML 2010: 631-638 - [c72]Jian Zhang, Hai Zhao, Bao-Liang Lu:
A comparative study on two large-scale hierarchical text classification tasks' solutions. ICMLC 2010: 3275-3280 - [c71]Yan-Ming Tang, Bao-Liang Lu:
Age Classification Combining Contour and Texture Feature. ICONIP (2) 2010: 493-500 - [c70]Qi Kong, Hai Zhao, Bao-Liang Lu:
Adaptive Ensemble Learning Strategy Using an Assistant Classifier for Large-Scale Imbalanced Patent Categorization. ICONIP (1) 2010: 601-608 - [c69]Tian-Xiang Wu, Bao-Liang Lu:
Multi-view Gender Classification Using Hierarchical Classifiers Structure. ICONIP (2) 2010: 625-632 - [c68]Minzhang Huang, Hai Zhao, Bao-Liang Lu:
Pruning Training Samples Using a Supervised Clustering Algorithm. ISNN (2) 2010: 250-257 - [c67]Gang Jin, Qi Kong, Jian Zhang, Xiaolin Wang, Cong Hui, Hai Zhao, Bao-Liang Lu:
Multiple Strategies for NTCIR-8 Patent Mining at BCMI. NTCIR 2010: 303-308 - [c66]Wen-Yun Yang, James T. Kwok, Bao-Liang Lu:
Spectral and Semidefinite Relaxation of the CLUHSIC Algorithm. SDM 2010: 106-117 - [c65]Cong Hui, Hai Zhao, Bao-Liang Lu, Yan Song:
An Empirical Study on Development Set Selection Strategy for Machine Translation Learning. WMT@ACL 2010: 67-71 - [e5]Liqing Zhang, Bao-Liang Lu, James Tin-Yau Kwok:
Advances in Neural Networks - ISNN 2010, 7th International Symposium on Neural Networks, ISNN 2010, Shanghai, China, June 6-9, 2010, Proceedings, Part I. Lecture Notes in Computer Science 6063, Springer 2010, ISBN 978-3-642-13277-3 [contents] - [e4]Liqing Zhang, Bao-Liang Lu, James Tin-Yau Kwok:
Advances in Neural Networks - ISNN 2010, 7th International Symposium on Neural Networks, ISNN 2010, Shanghai, China, June 6-9, 2010, Proceedings, Part II. Lecture Notes in Computer Science 6064, Springer 2010, ISBN 978-3-642-13317-6 [contents]
2000 – 2009
- 2009
- [j15]Dandan Song, Yang Yang, Bin Yu, Binglian Zheng, Zhidong Deng, Bao-Liang Lu, Xuemei Chen, Tao Jiang:
Computational prediction of novel non-coding RNAs in Arabidopsis thaliana. BMC Bioinform. 10(S-1) (2009) - [j14]Bao-Liang Lu, Xiaolin Wang, Masao Utiyama:
Incorporating prior knowledge into learning by dividing training data. Frontiers Comput. Sci. China 3(1): 109-122 (2009) - [j13]Yun Li, Bao-Liang Lu, Teng-Fei Zhang:
Combining Feature Selection with Extraction: Unsupervised Feature Selection Based on Principal Component Analysis. Int. J. Artif. Intell. Tools 18(6): 883-904 (2009) - [j12]Jing Li, Bao-Liang Lu:
An adaptive image Euclidean distance. Pattern Recognit. 42(3): 349-357 (2009) - [j11]Yun Li, Bao-Liang Lu:
Feature selection based on loss-margin of nearest neighbor classification. Pattern Recognit. 42(9): 1914-1921 (2009) - [c64]Zheng Ji, Bao-Liang Lu:
Gender Classification Based on Support Vector Machine with Automatic Confidence. ICONIP (1) 2009: 685-692 - [c63]Yue Wang, Bao-Liang Lu, Zhi-Fei Ye:
Module Combination based on Decision Tree in Min-max Modular Network. IJCCI 2009: 555-558 - [c62]Chao Ma, Bao-Liang Lu, Masao Utiyama:
Incorporating Prior Knowledge into Task Decomposition for Large-Scale Patent Classification. ISNN (2) 2009: 784-793 - [c61]Jia-Cheng Guo, Bao-Liang Lu, Zhiwei Li, Lei Zhang:
LogisticLDA: Regularizing Latent Dirichlet Allocation by Logistic Regression. PACLIC 2009: 160-169 - [c60]Weiming Liang, Changning Huang, Mu Li, Bao-Liang Lu:
Extracting Keyphrases from Chinese News Articles Using TextRank and Query Log Knowledge. PACLIC 2009: 733-740 - 2008
- [j10]Ke Wu, Bao-Liang Lu, Masao Utiyama, Hitoshi Isahara:
An empirical comparison of min-max-modular k -NN with different voting methods to large-scale text categorization. Soft Comput. 12(7): 647-655 (2008) - [c59]Ke Wu, Bao-Liang Lu:
A Refinement Framework for Cross Language Text Categorization. AIRS 2008: 401-411 - [c58]Wen-Yun Yang, Bao-Liang Lu:
String Kernels with Feature Selection for SVM Protein Classification. APBC 2008: 9-18 - [c57]Yuan-Peng Li, Bao-Liang Lu:
Semantic Similarity Definition over Gene Ontology by Further Mining of the Information Content. APBC 2008: 155-164 - [c56]Yang Yang, Bao-Liang Lu, Wen-Yun Yang:
Classification of Protein Sequences Based on Word Segmentation Methods. APBC 2008: 177-186 - [c55]Xiao-Chen Lian, Bao-Liang Lu:
Gender Classification by Combining Facial and Hair Information. ICONIP (2) 2008: 647-654 - [c54]Ke Wu, Xiaolin Wang, Bao-Liang Lu:
Cross Language Text Categorization Using a Bilingual Lexicon. IJCNLP 2008: 165-172 - [c53]Bin Xia, He Sun, Bao-Liang Lu:
Multi-view gender classification based on local Gabor binary mapping pattern and support vector machines. IJCNN 2008: 3388-3395 - [c52]Xiao-Lei Chu, Chao Ma, Jing Li, Bao-Liang Lu, Masao Utiyama, Hitoshi Isahara:
Large-scale patent classification with min-max modular support vector machines. IJCNN 2008: 3973-3980 - [c51]Jia-Wei Fu, Mu Li, Bao-Liang Lu:
Detecting Drowsiness in Driving Simulation Based on EEG. SJTU-TUB Joint Workshop 2008: 21-28 - [c50]Zhi-Fei Ye, Bao-Liang Lu, Cong Hui:
Patent Classification Using Parallel Min-Max Modular Support Vector Machine. SJTU-TUB Joint Workshop 2008: 157-167 - 2007
- [j9]Hui-Cheng Lian, Bao-Liang Lu:
Multi-View Gender Classification Using Multi-Resolution Local Binary Patterns and Support Vector Machines. Int. J. Neural Syst. 17(6): 479-487 (2007) - [j8]Yun Li, Bao-Liang Lu, Zhong-Fu Wu:
Hierarchical fuzzy filter method for unsupervised feature selection. J. Intell. Fuzzy Syst. 18(2): 157-169 (2007) - [c49]Jun Luo, Yong Ma, Erina Takikawa, Shihong Lao, Masato Kawade, Bao-Liang Lu:
Person-Specific SIFT Features for Face Recognition. ICASSP (2) 2007: 593-596 - [c48]Yang Yang, Bao-Liang Lu:
Incorporating Domain Knowledge into a Min-Max Modular Support Vector Machine for Protein Subcellular Localization. ICONIP (2) 2007: 827-836 - [c47]Jing Li, Bao-Liang Lu:
A Framework for Multi-view Gender Classification. ICONIP (1) 2007: 973-982 - [c46]Feng Zhou, Bao-Liang Lu:
Learning Concepts from Large-Scale Data Sets by Pairwise Coupling with Probabilistic Outputs. IJCNN 2007: 524-529 - [c45]Li-Chen Shi, Hong Yu, Bao-Liang Lu:
Semi-Supervised Clustering for Vigilance Analysis Based on EEG. IJCNN 2007: 1518-1523 - [c44]Zhi-Fei Ye, Bao-Liang Lu:
Learning Imbalanced Data Sets with a Min-Max Modular Support Vector Machine. IJCNN 2007: 1673-1678 - [c43]Yimin Wen, Bao-Liang Lu:
A Confident Majority Voting Strategy for Parallel and Modular Support Vector Machines. ISNN (3) 2007: 525-534 - [c42]Ke Wu, Bao-Liang Lu, Masao Uchiyama, Hitoshi Isahara:
A Probabilistic Approach to Feature Selection for Multi-class Text Categorization. ISNN (1) 2007: 1310-1317 - [c41]Yimin Wen, Bao-Liang Lu:
Incremental Learning of Support Vector Machines by Classifier Combining. PAKDD 2007: 904-911 - [c40]Ke Wu, Bao-Liang Lu:
Cross-Lingual Document Clustering. PAKDD 2007: 956-963 - [c39]Yun Li, Bao-Liang Lu:
Feature Selection for Identifying Critical Variables of Principal Components Based on K-Nearest Neighbor Rule. VISUAL 2007: 193-204 - [p1]Bao-Liang Lu, Jing Li:
A Min-Max Modular Network with Gaussian-Zero-Crossing Function. Trends in Neural Computation 2007: 285-313 - 2006
- [c38]Wen-Yun Yang, Bao-Liang Lu, Yang Yang:
A Comparative Study on Feature Extraction from Protein Sequences for Subcellular Localization Prediction. CIBCB 2006: 1-8 - [c37]Zhi-Gang Fan, Bao-Liang Lu:
Fast Learning for Statistical Face Detection. ICONIP (2) 2006: 187-196 - [c36]Yun Li, Bao-Liang Lu, Zhong-Fu Wu:
A Hybrid Method of Unsupervised Feature Selection Based on Ranking. ICPR (2) 2006: 687-690 - [c35]Jing Li, Bao-Liang Lu:
A New Supervised Clustering Algorithm Based on Min-Max Modular Network with Gaussian-Zero-Crossing Functions. IJCNN 2006: 786-793 - [c34]Ken Chen, Bao-Liang Lu, James T. Kwok:
Efficient Classification of Multi-label and Imbalanced Data using Min-Max Modular Classifiers. IJCNN 2006: 1770-1775 - [c33]Hui-Cheng Lian, Bao-Liang Lu:
Multi-view Gender Classification Using Local Binary Patterns and Support Vector Machines. ISNN (2) 2006: 202-209 - [c32]Jun Luo, Bao-Liang Lu:
Gender Recognition Using a Min-Max Modular Support Vector Machine with Equal Clustering. ISNN (2) 2006: 210-215 - [c31]Hai Zhao, Bao-Liang Lu:
A Modular Reduction Method for k-NN Algorithm with Self-recombination Learning. ISNN (1) 2006: 537-544 - [c30]Yang Yang, Bao-Liang Lu:
Prediction of Protein Subcellular Multi-locations with a Min-Max Modular Support Vector Machine. ISNN (2) 2006: 667-673 - [c29]Hai Zhao, Changning Huang, Mu Li, Bao-Liang Lu:
Effective Tag Set Selection in Chinese Word Segmentation via Conditional Random Field Modeling. PACLIC 2006 - [e3]Jun Wang, Zhang Yi, Jacek M. Zurada, Bao-Liang Lu, Hujun Yin:
Advances in Neural Networks - ISNN 2006, Third International Symposium on Neural Networks, Chengdu, China, May 28 - June 1, 2006, Proceedings, Part I. Lecture Notes in Computer Science 3971, Springer 2006, ISBN 3-540-34439-X [contents] - [e2]Jun Wang, Zhang Yi, Jacek M. Zurada, Bao-Liang Lu, Hujun Yin:
Advances in Neural Networks - ISNN 2006, Third International Symposium on Neural Networks, Chengdu, China, May 28 - June 1, 2006, Proceedings, Part II. Lecture Notes in Computer Science 3972, Springer 2006, ISBN 3-540-34437-3 [contents] - [e1]Jun Wang, Zhang Yi, Jacek M. Zurada, Bao-Liang Lu, Hujun Yin:
Advances in Neural Networks - ISNN 2006, Third International Symposium on Neural Networks, Chengdu, China, May 28 - June 1, 2006, Proceedings, Part III. Lecture Notes in Computer Science 3973, Springer 2006, ISBN 3-540-34482-9 [contents] - 2005
- [c28]Yang Yang, Bao-Liang Lu:
Extracting Features from Protein Sequences Using Chinese Segmentation Techniques for Subcellular Localization. CIBCB 2005: 288-295 - [c27]Zhi-Gang Fan, Bao-Liang Lu:
Fast Recognition of Multi-View Faces with Feature Selection. ICCV 2005: 76-81 - [c26]Jing Li, Bao-Liang Lu, Michinori Ichikawa:
An Algorithm for Pruning Redundant Modules in Min-Max Modular Network with GZC Function. ICNC (1) 2005: 293-302 - [c25]Hai Zhao, Bao-Liang Lu:
A General Procedure for Combining Binary Classifiers and Its Performance Analysis. ICNC (1) 2005: 303-312 - [c24]Zhi-Gang Fan, Bao-Liang Lu:
Multi-view Face Recognition with Min-Max Modular SVMs. ICNC (2) 2005: 396-399 - [c23]Hui-Cheng Lian, Bao-Liang Lu, Erina Takikawa, Satoshi Hosoi:
Gender Recognition Using a Min-Max Modular Support Vector Machine. ICNC (2) 2005: 438-441 - [c22]Feng-Yao Liu, Ke Wu, Hai Zhao, Bao-Liang Lu:
Fast text categorization with min-max modular support vector machines. IJCNN 2005: 570-575 - [c21]Hui-Cheng Lian, Bao-Liang Lu:
An algorithm for pruning redundant modules in min-ma modular network [min-ma read min-max]. IJCNN 2005: 1983-1988 - [c20]Hai Zhao, Bao-Liang Lu:
On efficient selection of binary classifiers for min-max modular classifier. IJCNN 2005: 3186-3191 - [c19]Hai Zhao, Bao-Liang Lu:
Improvement on Response Performance of Min-Max Modular Classifier by Symmetric Module Selection. ISNN (2) 2005: 39-44 - [c18]Jing Li, Bao-Liang Lu, Michinori Ichikawa:
Typical Sample Selection and Redundancy Reduction for Min-Max Modular Network with GZC Function. ISNN (1) 2005: 467-472 - [c17]Yang Yang, Bao-Liang Lu:
Structure Pruning Strategies for Min-Max Modular Network. ISNN (1) 2005: 646-651 - [c16]Yimin Wen, Bao-Liang Lu:
A Hierarchical and Parallel Method for Training Support Vector Machines. ISNN (1) 2005: 881-886 - [c15]Kai-An Wang, Hai Zhao, Bao-Liang Lu:
Task Decomposition Using Geometric Relation for Min-Max Modular SVMs. ISNN (1) 2005: 887-892 - 2004
- [j7]Bao-Liang Lu, Jonghan Shin, Michinori Ichikawa:
Massively parallel classification of single-trial EEG signals using a min-max Modular neural network. IEEE Trans. Biomed. Eng. 51(3): 551-558 (2004) - [c14]Hai Zhao, Bao-Liang Lu:
A Modular k-Nearest Neighbor Classification Method for Massively Parallel Text Categorization. CIS 2004: 867-872 - [c13]Bin Huang, Bao-Liang Lu:
Fault Diagnosis for Industrial Images Using a Min-Max Modular Neural Network. ICONIP 2004: 842-847 - [c12]Zhi-Gang Fan, Kai-An Wang, Bao-Liang Lu:
Feature Selection for Fast Image Classification with Support Vector Machines. ICONIP 2004: 1026-1031 - [c11]Bao-Liang Lu, Kai-An Wang, Masao Utiyama, Hitoshi Isahara:
A part-versus-part method for massively parallel training of support vector machines. IJCNN 2004 - [c10]Yimin Wen, Bao-Liang Lu:
A Cascade Method for Reducing Training Time and the Number of Support Vectors. ISNN (1) 2004: 480-486 - [c9]Zhi-Gang Fan, Bao-Liang Lu:
An Adjusted Gaussian Skin-Color Model Based on Principal Component Analysis. ISNN (1) 2004: 804-809 - [c8]Hai Zhao, Bao-Liang Lu:
Analysis of Fault Tolerance of a Combining Classifier. ISNN (1) 2004: 888-893 - 2003
- [j6]Bao-Liang Lu, Qing Ma, Michinori Ichikawa, Hitoshi Isahara:
Efficient Part-of-Speech Tagging with a Min-Max Modular Neural-Network Model. Appl. Intell. 19(1-2): 65-81 (2003) - [j5]Bao-Liang Lu, Koji Ito:
Converting general nonlinear programming problems into separable programming problems with feedforward neural networks. Neural Networks 16(7): 1059-1074 (2003) - 2002
- [j4]Qing Ma, Bao-Liang Lu, Hitoshi Isahara, Michinori Ichikawa:
Part of speech tagging with min-max modular neural networks. Syst. Comput. Jpn. 33(7): 30-39 (2002) - 2001
- [j3]Jonghan Shin, Bao-Liang Lu, Arkadi Talnov, Gen Matsumoto, Jurij Brankack:
Reading auditory discrimination behaviour of freely moving rats from hippocampal EEG. Neurocomputing 38-40: 1557-1566 (2001) - [c7]Bao-Liang Lu, Jonghan Shin, Michinori Ichikawa:
Massively Parallel Classification of EEG Signals Using Min-Max Modular Neural Networks. ICANN 2001: 601-608 - [c6]Qing Ma, Bao-Liang Lu, Masaki Murata, Michinori Ichikawa, Hitoshi Isahara:
On-Line Error Detection of Annotated Corpus Using Modular Neural Networks. ICANN 2001: 1185-1192 - 2000
- [c5]Bao-Liang Lu, Michinori Ichikawa:
Emergence of Learning: An Approach to Coping with NP-Complete Problems in Learning. IJCNN (4) 2000: 159-164
1990 – 1999
- 1999
- [j2]Bao-Liang Lu, Masami Ito:
Task decomposition and module combination based on class relations: a modular neural network for pattern classification. IEEE Trans. Neural Networks 10(5): 1244-1256 (1999) - [j1]Bao-Liang Lu, Hajime Kita, Yoshikazu Nishikawa:
Inverting feedforward neural networks using linear and nonlinear programming. IEEE Trans. Neural Networks 10(6): 1271-1290 (1999) - 1998
- [c4]Bao-Liang Lu, Masami Ito:
Decomposition and Parallel Learning of Imbalanced Classification Problems by Min-Max Modular Neural Network. ICONIP 1998: 199-202 - [c3]Bao-Liang Lu, Masami Ito:
Decomposing and Parallel Learning of Large-Scale Pattern Recognition Problems using Min-Max Modular Neural Network. NC 1998: 703-709 - 1997
- [c2]Bao-Liang Lu, Masami Ito:
Task Decomposition Based on Class Relations: A Modular Neural Network Architecture for Pattern Classification. IWANN 1997: 330-339 - 1995
- [c1]Bao-Liang Lu, Koji Ito:
Regularization of inverse kinematics for redundant manipulators using neural network inversions. ICNN 1995: 2726-2731
Coauthor Index
James T. Kwok
aka: James Tin-Yau Kwok
aka: James Tin-Yau Kwok
[c228] [c227] [c226] [c225] [c224] [c223] [c222] [c221] [c220] [c219] [c218] [c217] [c216] [c215] [c214] [c212] [c210] [i22] [i19] [c209] [c208] [c207] [c206] [c205] [c204] [c203] [c202] [c201] [c200] [c199] [c198] [c197] [i18] [j61] [j59] [c195] [c192] [c190] [j54] [j50] [i17] [j48] [j47] [c169] [c168] [c164] [c163] [c162] [c161] [i11] [j45] [c159] [c158] [c157] [c155] [c154] [c153] [i9] [j41] [c151] [c150] [c149] [c148] [c147] [c145] [c144] [c143] [i8] [j40] [c139] [c138] [c137] [c135] [c134] [i6] [i5] [i2] [j33] [c132] [c128] [c126] [c124] [c123] [c122] [c120] [c119] [c117] [c116] [c114] [c113] [c112]
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