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ORCID11. WSOM 2016: Houston, Texas, USA
- Erzsébet Merényi, Michael J. Mendenhall, Patrick O'Driscoll:
Advances in Self-Organizing Maps and Learning Vector Quantization - Proceedings of the 11th International Workshop WSOM 2016, Houston, Texas, USA, January 6-8, 2016. Advances in Intelligent Systems and Computing 428, Springer 2016, ISBN 978-3-319-28517-7
Self-Organizing Map Learning, Visualization, and Quality Assessment
- Marie Cottrell, Madalina Olteanu, Fabrice Rossi
, Nathalie Villa-Vialaneix:
Theoretical and Applied Aspects of the Self-Organizing Maps. 3-26 - Jérôme Mariette, Nathalie Villa-Vialaneix:
Aggregating Self-Organizing Maps with Topology Preservation. 27-37 - Alfred Ultsch, Martin Behnisch, Jörn Lötsch:
ESOM Visualizations for Quality Assessment in Clustering. 39-48 - Lutz Hamel:
SOM Quality Measures: An Efficient Statistical Approach. 49-59 - Denny, William Gozali, Ruli Manurung:
SOM Training Optimization Using Triangle Inequality. 61-71 - Madalina Olteanu, Nathalie Villa-Vialaneix:
Sparse Online Self-Organizing Maps for Large Relational Data. 73-82
Clustering and Time Series Analysis with Self-Organizing Maps and Neural Gas
- Yaser Moazzen, Kadim Tasdemir:
A Neural Gas Based Approximate Spectral Clustering Ensemble. 85-93 - Jean-Charles Lamirel:
Reliable Clustering Quality Estimation from Low to High Dimensional Data. 95-105 - Jorge R. Vergara, Pablo A. Estévez, Álvaro Serrano:
Segment Growing Neural Gas for Nonlinear Time Series Analysis. 107-117 - Rigoberto Fonseca-Delgado, Pilar Gómez-Gil:
Modeling Diversity in Ensembles for Time-Series Prediction Based on Self-Organizing Maps. 119-128
Applications in Control, Planning, and Dimensionality Reduction, and Hardware for Self-Organizing Maps
- Paulo Henrique Muniz Ferreira, Aluízio Fausto Ribeiro Araújo:
Modular Self-Organizing Control for Linear and Nonlinear Systems. 131-141 - Jan Faigl:
On Self-Organizing Map and Rapidly-Exploring Random Graph in Multi-Goal Planning. 143-153 - Oliver Kramer:
Dimensionality Reduction Hybridizations with Multi-dimensional Scaling. 155-163 - Mehdi Abadi, Slavisa Jovanovic, Khaled Ben Khalifa, Serge Weber, Mohamed Hédi Bedoui:
A Scalable Flexible SOM NoC-Based Hardware Architecture. 165-175 - Humberto I. Fontinele, Davyd B. Melo, Guilherme A. Barreto:
Local Models for Learning Inverse Kinematics of Redundant Robots: A Performance Comparison. 177-187
Self-Organizing Maps in Neuroscience and Medical Applications
- Risto Miikkulainen:
Using SOMs to Gain Insight into Human Language Processing. 191 - Nahed Alowadi, Yuan Shen, Peter Tiño:
Prototype-Based Spatio-Temporal Probabilistic Modelling of fMRI Data. 193-203 - Deborah Mudali, Michael Biehl, Klaus Leonard Leenders, Jos B. T. M. Roerdink:
LVQ and SVM Classification of FDG-PET Brain Data. 205-215 - Axel Wismüller, Anas Z. Abidin, Adora M. DSouza, Mahesh B. Nagarajan:
Mutual Connectivity Analysis (MCA) for Nonlinear Functional Connectivity Network Recovery in the Human Brain Using Convergent Cross-Mapping and Non-metric Clustering. 217-226 - Benjamin D. Kramer, Dylan P. Losey, Marcia K. O'Malley:
SOM and LVQ Classification of Endovascular Surgeons Using Motion-Based Metrics. 227-237 - Masaaki Ohkita, Heizo Tokutaka, Nobuhiko Kasezawa, Eikou Gonda:
Visualization and Practical Use of Clinical Survey Medical Examination Results. 239-249 - Patrick O'Driscoll, Erzsébet Merényi, Christof Karmonik, Robert G. Grossman:
The Effect of SOM Size and Similarity Measure on Identification of Functional and Anatomical Regions in fMRI Data. 251-263
Learning Vector Quantization Theories and Applications I
- Pablo A. Estévez:
Big Data Era Challenges and Opportunities in Astronomy - How SOM/LVQ and Related Learning Methods Can Contribute? 267 - Thomas Villmann, Marika Kaden, Andrea Bohnsack, J.-M. Villmann, T. Drogies, Sascha Saralajew, Barbara Hammer:
Self-Adjusting Reject Options in Prototype Based Classification. 269-279 - David Nebel, Thomas Villmann:
Optimization of Statistical Evaluation Measures for Classification by Median Learning Vector Quantization. 281-291 - Matthias Gay, Marika Kaden, Michael Biehl, Alexander Lampe, Thomas Villmann:
Complex Variants of GLVQ Based on Wirtinger's Calculus. 293-303 - David Nova, Pablo A. Estévez:
A Study on GMLVQ Convex and Non-convex Regularization. 305-314
Learning Vector Quantization Theories and Applications II
- Friedrich Melchert, Udo Seiffert, Michael Biehl:
Functional Representation of Prototypes in LVQ and Relevance Learning. 317-327 - Ernest Mwebaze, Michael Biehl:
Prototype-Based Classification for Image Analysis and Its Application to Crop Disease Diagnosis. 329-339 - Kerstin Bunte, Marika Kaden, Frank-Michael Schleif:
Low-Rank Kernel Space Representations in Prototype Learning. 341-353 - Jonathon Climer, Michael J. Mendenhall:
Dynamic Prototype Addition in Generalized Learning Vector Quantization. 355-368
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