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ORCID17th PKDD / 24th ECML 2013: Prague, Czech Republic
- Hendrik Blockeel
, Kristian Kersting, Siegfried Nijssen, Filip Zelezný:
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2013, Prague, Czech Republic, September 23-27, 2013, Proceedings, Part II. Lecture Notes in Computer Science 8189, Springer 2013, ISBN 978-3-642-40990-5
Social Network Analysis
- Tengfei Ji, Dongqing Yang, Jun Gao:
Incremental Local Evolutionary Outlier Detection for Dynamic Social Networks. 1-15 - Yuxiao Dong, Jie Tang, Tiancheng Lou, Bin Wu, Nitesh V. Chawla:
How Long Will She Call Me? Distribution, Social Theory and Duration Prediction. 16-31 - Nikolaj Tatti, Aristides Gionis:
Discovering Nested Communities. 32-47 - Yasir Mehmood, Nicola Barbieri, Francesco Bonchi, Antti Ukkonen:
CSI: Community-Level Social Influence Analysis. 48-63
Natural Language Processing and Information Extraction
- Guido Boella, Luigi Di Caro:
Supervised Learning of Syntactic Contexts for Uncovering Definitions and Extracting Hypernym Relations in Text Databases. 64-79 - William Darling, Cédric Archambeau, Shachar Mirkin, Guillaume Bouchard:
Error Prediction with Partial Feedback. 80-94 - Moisés Goldszmidt, Marc Najork, Stelios Paparizos:
Boot-Strapping Language Identifiers for Short Colloquial Postings. 95-111
Ranking and Recommender Systems
- Sébastien Destercke:
A Pairwise Label Ranking Method with Imprecise Scores and Partial Predictions. 112-127 - Karthik Raman, Thorsten Joachims:
Learning Socially Optimal Information Systems from Egoistic Users. 128-144 - Julien Delporte, Alexandros Karatzoglou, Tomasz Matuszczyk, Stéphane Canu:
Socially Enabled Preference Learning from Implicit Feedback Data. 145-160 - Sheng Gao, Hao Luo, Da Chen, Shantao Li, Patrick Gallinari, Jun Guo:
Cross-Domain Recommendation via Cluster-Level Latent Factor Model. 161-176 - Deguang Kong, Miao Zhang, Chris H. Q. Ding:
Minimal Shrinkage for Noisy Data Recovery Using Schatten-p Norm Objective. 177-193
Matrix and Tensor Analysis
- Suriya Gunasekar, Ayan Acharya, Neeraj Gaur, Joydeep Ghosh:
Noisy Matrix Completion Using Alternating Minimization. 194-209 - Dehua Liu, Tengfei Zhou, Hui Qian, Congfu Xu, Zhihua Zhang:
A Nearly Unbiased Matrix Completion Approach. 210-225 - Hongyang Zhang, Zhouchen Lin, Chao Zhang:
A Counterexample for the Validity of Using Nuclear Norm as a Convex Surrogate of Rank. 226-241 - Qing Liao, Qian Zhang:
Efficient Rank-one Residue Approximation Method for Graph Regularized Non-negative Matrix Factorization. 242-255 - Kleanthis-Nikolaos Kontonasios, Jilles Vreeken, Tijl De Bie:
Maximum Entropy Models for Iteratively Identifying Subjectively Interesting Structure in Real-Valued Data. 256-271 - Maximilian Nickel, Volker Tresp:
An Analysis of Tensor Models for Learning on Structured Data. 272-287 - Haiping Lu:
Learning Modewise Independent Components from Tensor Data Using Multilinear Mixing Model. 288-303
Structured Output Prediction, Multi-label and Multi-task Learning
- Matthew B. Blaschko, Wojciech Zaremba, Arthur Gretton:
Taxonomic Prediction with Tree-Structured Covariances. 304-319 - Zubin Abraham, Pang-Ning Tan, Perdinan, Julie Winkler, Shiyuan Zhong, Malgorzata Liszewska:
Position Preserving Multi-Output Prediction. 320-335 - Chengtao Li, Jianwen Zhang, Zheng Chen:
Structured Output Learning with Candidate Labels for Local Parts. 336-352 - Xin Jin, Fuzhen Zhuang, Shuhui Wang, Qing He, Zhongzhi Shi:
Shared Structure Learning for Multiple Tasks with Multiple Views. 353-368 - Ayan Acharya, Aditya Rawal, Raymond J. Mooney, Eduardo R. Hruschka:
Using Both Latent and Supervised Shared Topics for Multitask Learning. 369-384 - Rodrigo C. Barros, Ricardo Cerri, Alex Alves Freitas, André Carlos Ponce de Leon Ferreira de Carvalho:
Probabilistic Clustering for Hierarchical Multi-Label Classification of Protein Functions. 385-400 - Kai-Wei Chang, Vivek Srikumar, Dan Roth:
Multi-core Structural SVM Training. 401-416 - Yuhong Guo, Dale Schuurmans:
Multi-label Classification with Output Kernels. 417-432
Transfer Learning
- Amaury Habrard, Jean-Philippe Peyrache, Marc Sebban:
Boosting for Unsupervised Domain Adaptation. 433-448 - Haitham Bou-Ammar, Decebal Constantin Mocanu, Matthew E. Taylor, Kurt Driessens, Karl Tuyls, Gerhard Weiss:
Automatically Mapped Transfer between Reinforcement Learning Tasks via Three-Way Restricted Boltzmann Machines. 449-464
Bayesian Learning
- Adway Mitra, Ranganath B. N., Indrajit Bhattacharya:
A Layered Dirichlet Process for Hierarchical Segmentation of Sequential Grouped Data. 465-482 - Wei Liu, Jeffrey Chan, James Bailey, Christopher Leckie, Fang Chen, Kotagiri Ramamohanarao:
A Bayesian Classifier for Learning from Tensorial Data. 483-498 - Kazuto Fukuchi, Jun Sakuma, Toshihiro Kamishima:
Prediction with Model-Based Neutrality. 499-514 - M. Ehsan Abbasnejad, Edwin V. Bonilla, Scott Sanner:
Decision-Theoretic Sparsification for Gaussian Process Preference Learning. 515-530 - Konstantinos Bousmalis, Stefanos Zafeiriou, Louis-Philippe Morency, Maja Pantic, Zoubin Ghahramani:
Variational Hidden Conditional Random Fields with Coupled Dirichlet Process Mixtures. 531-547 - Václav Smídl, Ondrej Tichý:
Sparsity in Bayesian Blind Source Separation and Deconvolution. 548-563 - Priyanka Agrawal, Lavanya Sita Tekumalla, Indrajit Bhattacharya:
Nested Hierarchical Dirichlet Process for Nonparametric Entity-Topic Analysis. 564-579
Graphical Models
- Shuo Yang, Sriraam Natarajan:
Knowledge Intensive Learning: Combining Qualitative Constraints with Causal Independence for Parameter Learning in Probabilistic Models. 580-595 - Song Liu, John A. Quinn, Michael U. Gutmann, Masashi Sugiyama:
Direct Learning of Sparse Changes in Markov Networks by Density Ratio Estimation. 596-611 - Robert Peharz, Bernhard C. Geiger, Franz Pernkopf:
Greedy Part-Wise Learning of Sum-Product Networks. 612-627 - Ramnath Balasubramanyan, Bhavana Bharat Dalvi, William W. Cohen:
From Topic Models to Semi-supervised Learning: Biasing Mixed-Membership Models to Exploit Topic-Indicative Features in Entity Clustering. 628-642
Nearest-Neighbor Methods
- Nenad Tomasev, Dunja Mladenic:
Hub Co-occurrence Modeling for Robust High-Dimensional kNN Classification. 643-659 - Yan-Ming Zhang, Kaizhu Huang, Guanggang Geng, Cheng-Lin Liu:
Fast kNN Graph Construction with Locality Sensitive Hashing. 660-674 - Murat Semerci, Ethem Alpaydin:
Mixtures of Large Margin Nearest Neighbor Classifiers. 675-688
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