Kohonen MapsE. Oja, Samuel Kaski 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. |
Contents
| 1 | |
| 15 | |
| 33 | |
Chapter 4 From aggregation operators to soft Learning Vector Quantization and clustering algorithms ... | 47 |
Chapter 5 Active learning in SelfOrganizing Maps | 57 |
Chapter 6 Point prototype generation and classifier design | 71 |
Chapter 7 SelfOrganizing Maps on nonEuclidean spaces | 97 |
Chapter 8 SelfOrganising Maps for pattern recognition | 111 |
An approach to optimize surface component mounting on a printed circuit board | 219 |
Chapter 18 SelfOrganising Maps in computer aided design of electronic circuits | 231 |
Chapter 19 Modeling selforganization in the visual cortex | 243 |
Chapter 20 A spatiotemporal memory based on SOMs with activity diffusion | 253 |
Chapter 21 Advances in modeling cortical maps | 267 |
Chapter 22 Topology preservation in SelfOrganizing Maps | 279 |
Chapter 23 Secondorder learning in SelfOrganizing Maps | 293 |
Chapter 24 Energy functions for SelfOrganizing Maps | 303 |
Chapter 9 Tree structured SelfOrganizing Maps | 121 |
Chapter 10 Growing selforganizing networks history status quo and perspectives | 131 |
Chapter 11 Kohonen SelfOrganizing Map with quantized weights | 145 |
Chapter 12 On the optimization of SelfOrganizing Maps by genetic algorithms | 157 |
Chapter 13 Self organization of a massive text document collection | 171 |
Chapter 14 Document classification with SelfOrganizing Maps | 183 |
Chapter 15 Navigation in databases using SelfOrganising Maps | 197 |
Chapter 16 A SOMbased sensing approach to robotic manipulation tasks | 207 |
Chapter 25 LVQ and single trial EEG classification | 317 |
Chapter 26 SelfOrganizing Map in categorization of voice qualities | 329 |
A worked example of the analysis of cosmetics using Raman spectroscopy | 335 |
Chapter 28 SelfOrganizing Maps for contentbased image database retrieval | 349 |
Chapter 29 Indexing audio documents by using latent semantic analysis and SOM | 363 |
Chapter 30 SelfOrganizing Map in analysis of largescale industrial systems | 375 |
| 389 | |
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Common terms and phrases
1999 Elsevier Science activity analysis Artificial Neural Networks autoencoder average B.V. All rights classes classification components corresponding cortex Data Mining data set database defined dimension dimensional dissimilarity distance editors 1999 Elsevier Elsevier Science B.V. energy function error Euclidean Euclidean distance feature vectors Figure Gaussian Hebbian learning hierarchical hyperbolic IEEE input space Kaski Kohonen Maps labeled lateral connections lattice layer learning rule learning vector quantization lipsticks matrix measure method neighborhood function neighboring Neural Computation neurons nodes º º obtained optimization output space parameters patterns PicSOM principal curve problem prototypes query R-prototypes Raman receptive fields reformulation function representation represented retrieval sample scheduling selection Self-Organizing Feature Maps self-organizing map signals similar SOMs spectra structure temporal Teuvo Kohonen topology topology preservation TS-SOM two-dimensional unit unsupervised learning update variables vector quantization visual Voronoi diagram weight vector winner
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References to this book
Multimedia Data Mining and Knowledge Discovery Valery A. Petrushin,Latifur Khan No preview available - 2007 |
Advances in Neural Networks - ISNN 2005: Second International Symposium on ... Jun Wang,Xiaofeng Liao No preview available - 2005 |