Kohonen Maps

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E. Oja, Samuel Kaski
Elsevier, Jul 2, 1999 - Computers - 400 pages
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The Self-Organizing Map, or Kohonen Map, is one of the most widely used neural network algorithms, with thousands of applications covered in the literature. It was one of the strong underlying factors in the popularity of neural networks starting in the early 80's. Currently this method has been included in a large number of commercial and public domain software packages. In this book, top experts on the SOM method take a look at the state of the art and the future of this computing paradigm.

The 30 chapters of this book cover the current status of SOM theory, such as connections of SOM to clustering, classification, probabilistic models, and energy functions. Many applications of the SOM are given, with data mining and exploratory data analysis the central topic, applied to large databases of financial data, medical data, free-form text documents, digital images, speech, and process measurements. Biological models related to the SOM are also discussed.


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Demographic study of the Rhône valley The domestic consumption of the Canadian families
Finding value in markets that are expensive
Chapter 3 Data mining and knowledge discovery with emergent SelfOrganizing Feature Maps for multivariate time series ...
Chapter 4 From aggregation operators to soft Learning Vector Quantization and clustering algorithms ...
Chapter 5 Active learning in SelfOrganizing Maps
Chapter 6 Point prototype generation and classifier design
Chapter 7 SelfOrganizing Maps on nonEuclidean spaces
Chapter 8 SelfOrganising Maps for pattern recognition
An approach to optimize surface component mounting on a printed circuit board
Chapter 18 SelfOrganising Maps in computer aided design of electronic circuits
Chapter 19 Modeling selforganization in the visual cortex
Chapter 20 A spatiotemporal memory based on SOMs with activity diffusion
Chapter 21 Advances in modeling cortical maps
Chapter 22 Topology preservation in SelfOrganizing Maps
Chapter 23 Secondorder learning in SelfOrganizing Maps
Chapter 24 Energy functions for SelfOrganizing Maps

Chapter 9 Tree structured SelfOrganizing Maps
Chapter 10 Growing selforganizing networks history status quo and perspectives
Chapter 11 Kohonen SelfOrganizing Map with quantized weights
Chapter 12 On the optimization of SelfOrganizing Maps by genetic algorithms
Chapter 13 Self organization of a massive text document collection
Chapter 14 Document classification with SelfOrganizing Maps
Chapter 15 Navigation in databases using SelfOrganising Maps
Chapter 16 A SOMbased sensing approach to robotic manipulation tasks
Chapter 25 LVQ and single trial EEG classification
Chapter 26 SelfOrganizing Map in categorization of voice qualities
A worked example of the analysis of cosmetics using Raman spectroscopy
Chapter 28 SelfOrganizing Maps for contentbased image database retrieval
Chapter 29 Indexing audio documents by using latent semantic analysis and SOM
Chapter 30 SelfOrganizing Map in analysis of largescale industrial systems
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Page 361 - Visualseek: A fully automated content-based image query system," in Proceedings of ACM Multimedia, (Boston, MA), Nov.
Page 373 - Renals. The Use of Recurrent Neural Networks in Continuous Speech Recognition. In CH Lee, KK Paliwal, and FK Soong, editors, Automatic Speech and Speaker Recognition - Advanced Topics, chapter 19. Kluwer Academic Publishers, 1995.
Page 361 - T. Honkela, S. Kaski, K. Lagus, and T. Kohonen, "WEBSOM — self-organizing maps of document collections,

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About the author (1999)

Samuel Kaski received the DSc (PhD) degree in Computer Science from Helsinki University of Technology, Finland, in 1997. He is currently a Professor at Aalto University, the Director of Helsinki Institute for Information Technology HIIT, Aalto University and University of Helsinki, Finland, and the Director of Finnish Centre of Excellence in Computational Inference Research COIN. He is an action editor of the Journal of Machine Learning Research, and has chaired several conferences including AISTATS 2014. He has published over 200 peer-reviewed papers and supervised 18 PhD theses. His current research interests include statistical machine learning, computational biology and medicine, information visualization, and exploratory information retrieval.

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