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ORCID13th WSOM+ 2019: Barcelona, Spain
- Alfredo Vellido, Karina Gibert, Cecilio Angulo, José David Martín-Guerrero:
Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization - Proceedings of the 13th International Workshop, WSOM+ 2019, Barcelona, Spain, June 26-28, 2019. Advances in Intelligent Systems and Computing 976, Springer 2020, ISBN 978-3-030-19641-7
Self-organizing Maps: Theoretical Developments
- Jérémy Fix, Hervé Frezza-Buet:
Look and Feel What and How Recurrent Self-Organizing Maps Learn. 3-12 - Xiaofeng Ma, Michael Kirby, Chris Peterson:
Self-Organizing Mappings on the Flag Manifold. 13-22 - Lars Elend
, Oliver Kramer:
Self-Organizing Maps with Convolutional Layers. 23-32 - Bernard Girau, Andres Upegui:
Cellular Self-Organising Maps - CSOM. 33-43 - Joshua Taylor, Erzsébet Merényi:
A Probabilistic Method for Pruning CADJ Graphs with Applications to SOM Clustering. 44-54
Practical Applications of Self-Organizing Maps, Learning Vector Quantization and Clustering
- Maia Rosengarten, Sowmya Ramachandran:
SOM-Based Anomaly Detection and Localization for Space Subsystems. 57-69 - Lorena A. Santos, Karine Reis Ferreira, Michelle Cristina Araújo Picoli, Gilberto Câmara:
Self-Organizing Maps in Earth Observation Data Cubes Analysis. 70-79 - Alberto Nogales, Álvaro José García-Tejedor, Noemy Martín Sanz, Teresa de Dios Alija:
Competencies in Higher Education: A Feature Analysis with Self-Organizing Maps. 80-89 - Zefeng Bai, Nitin Jain, Ying Wang, Dominique Haughton:
Using SOM-Based Visualization to Analyze the Financial Performance of Consumer Discretionary Firms. 90-99 - Yann Bernard, Nicolas Hueber, Bernard Girau:
Novelty Detection with Self-Organizing Maps for Autonomous Extraction of Salient Tracking Features. 100-109 - Alaa Ali Hameed, Naim Ajlouni, Bekir Karlik:
Robust Adaptive SOMs Challenges in a Varied Datasets Analytics. 110-119 - Marie Cottrell, Cynthia Faure, Jérôme Lacaille, Madalina Olteanu:
Detection of Abnormal Flights Using Fickle Instances in SOM Maps. 120-129 - Diego P. Sousa, Guilherme A. Barreto, Charles C. Cavalcante, Cláudio M. S. Medeiros:
LVQ-type Classifiers for Condition Monitoring of Induction Motors: A Performance Comparison. 130-139 - Madalina Olteanu, Jean-Charles Lamirel:
When Clustering the Multiscalar Fingerprint of the City Reveals Its Segregation Patterns. 140-149 - Ashutosh Karna, Karina Gibert:
Using Hierarchical Clustering to Understand Behavior of 3D Printer Sensors. 150-159 - Henry Kvinge, Michael Kirby, Chris Peterson, Chad Eitel, Tod Clapp:
A Walk Through Spectral Bands: Using Virtual Reality to Better Visualize Hyperspectral Data. 160-165 - Jan Faigl, Milos Prágr:
Incremental Traversability Assessment Learning Using Growing Neural Gas Algorithm. 166-176
Learning Vector Quantization: Theoretical Developments
- Thomas Villmann, Jensun Ravichandran, Andrea Villmann, David Nebel, Marika Kaden:
Investigation of Activation Functions for Generalized Learning Vector Quantization. 179-188 - Sascha Saralajew, Lars Holdijk, Maike Rees, Thomas Villmann:
Robustness of Generalized Learning Vector Quantization Models Against Adversarial Attacks. 189-199 - Moritz Heusinger, Christoph Raab, Frank-Michael Schleif:
Passive Concept Drift Handling via Momentum Based Robust Soft Learning Vector Quantization. 200-209 - Michael Biehl, Fthi Abadi, Christina Göpfert, Barbara Hammer:
Prototype-Based Classifiers in the Presence of Concept Drift: A Modelling Framework. 210-221
Theoretical Developments in Clustering, Deep Learning and Neural Gas
- Mohammed Oualid Attaoui, Mustapha Lebbah, Nabil Keskes, Hanene Azzag, Mohammed Ghesmoune:
Soft Subspace Topological Clustering over Evolving Data Stream. 225-230 - Sascha Fleer, Helge J. Ritter:
Solving a Tool-Based Interaction Task Using Deep Reinforcement Learning with Visual Attention. 231-240 - David N. Coelho, Guilherme A. Barreto:
Approximate Linear Dependence as a Design Method for Kernel Prototype-Based Classifiers. 241-250 - Shannon Stiverson, Michael Kirby, Chris Peterson:
Subspace Quantization on the Grassmannian. 251-260 - Tina Geweniger, Thomas Villmann:
Variants of Fuzzy Neural Gas. 261-270 - Rudolf J. Szadkowski, Jan Drchal, Jan Faigl:
Autoencoders Covering Space as a Life-Long Classifier. 271-281
Life Science Applications
- Camden Jansen, Ali Mortazavi:
Progressive Clustering and Characterization of Increasingly Higher Dimensional Datasets with Living Self-organizing Maps. 285-293 - Patrick Riley, Iván Olier, Marc Rea, Paulo Lisboa, Sandra Ortega-Martorell:
A Voting Ensemble Method to Assist the Diagnosis of Prostate Cancer Using Multiparametric MRI. 294-303 - Meenal Srivastava, Iván Olier, Patrick Riley, Paulo Lisboa, Sandra Ortega-Martorell:
Classifying and Grouping Mammography Images into Communities Using Fisher Information Networks to Assist the Diagnosis of Breast Cancer. 304-313 - Adrián Bazaga, Alfredo Vellido:
Network Community Cluster-Based Analysis for the Identification of Potential Leukemia Drug Targets. 314-323 - Thomas Villmann, Marika Kaden, Szymon Wasik, Mateusz Kudla, Kaja Gutowska, Andrea Villmann, Jacek Blazewicz:
Searching for the Origins of Life - Detecting RNA Life Signatures Using Learning Vector Quantization. 324-333 - Gen Niina, Heizo Tokutaka, Masaaki Ohkita, Nobuhiko Kasezawa:
Simultaneous Display of Front and Back Sides of Spherical SOM for Health Data Analysis. 334-339
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