Kohonen Maps

E. Oja, Samuel Kaski

• Publisher: Elsevier Science
• Year: 1999
• Print ISBN: 9780444502704
• eBook ISBN: 9780080535296
• Topic: Computer Science
• Subtopic: Neural Networks
• Index: Computer Science

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.

Table of contents

• Front Cover
• Kohonen Maps
• Copyright Page
• Preface: Kohonen Maps
• Table of Contents
• Chapter 1. Analyzing and representing multidimensional quantitative and qualitative data: Demographic study of the Rhône valley. The domestic consumption of the Canadian families
• Chapter 2. Value maps: Finding value in markets that are expensive
• Chapter 3. Data mining and knowledge discovery with emergent Self-Organizing Feature Maps for multivariate time series
• Chapter 4. From aggregation operators to soft Learning Vector Quantization and clustering algorithms
• Chapter 5. Active learning in Self-Organizing Maps⤀
• Chapter 6. Point prototype generation and classifier design
• Chapter 7. Self-Organizing Maps on non-Euclidean spaces
• Chapter 8. Self-Organising Maps for pattern recognition
• Chapter 9. Tree structured Self-Organizing Maps
• Chapter 10. Growing self-organizing networks — history, status quo, and perspectives
• Chapter 11. Kohonen Self-Organizing Map with quantized weights
• Chapter 12. On the optimization of Self-Organizing Maps by genetic algorithms
• Chapter 13. Self organization of a massive text document collection
• Chapter 14. Document classification with Self-Organizing Maps
• Chapter 15. Navigation in databases using Self-Organising Maps
• Chapter 16. A SOM-based sensing approach to robotic manipulation tasks
• Chapter 17. SOM-TSP: An approach to optimize surface component mounting on a printed circuit board
• Chapter 18. Self-Organising Maps in computer aided design of electronic circuits
• Chapter 19. Modeling self-organization in the visual cortex
• Chapter 20. A spatio-temporal memory based on SOMs with activity diffusion
• Chapter 21. Advances in modeling cortical maps
• Chapter 22. Topology preservation in Self-Organizing Maps
• Chapter 23. Second-order learning in Self-Organizing Maps
• Chapter 24. Energy functions for Self-Organizing Maps
• Chapter 25. LVQ and single trial EEG classification
• Chapter 26. Self-Organizing Map in categorization of voice qualities⤀
• Chapter 27. Chemometric analyses with Self Organising Feature Maps: A worked example of the analysis of cosmetics using Raman spectroscopy
• Chapter 28. Self-Organizing Maps for content-based image database retrieval
• Chapter 29. Indexing audio documents by using latent semantic analysis and SOM
• Chapter 30. Self-Organizing Map in analysis of large-scale industrial systems
• Keyword index