| Edition |
1st ed. |
| Description |
1 online resource (ix, 390 pages) : illustrations |
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text txt rdacontent |
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computer c rdamedia |
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online resource cr rdacarrier |
| Summary |
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 |
<IT>Selected papers only.</IT>Preface: Kohonen Maps. Table of contents. Analyzing and representing multidimentional quantitative and qualitative data: Demographic study of the Rhne valley. The domeatic consumption of the Canadian families. (M. Cottrell, P. Gaubert, P. Letremy, P. Rousset). Value maps: Finding value in markets that are expensive (G.J. Deboeck). Data mining and knowledge discovery with emergent Self-Organizing Feature Maps for multivariate time series (A. Ultsch). Tree structured Self-Organizing Maps (P. Koikkalainen). On the optimization of Self-Organizing Maps by genetic algorithms (D. Polani). Self organization of a massive text document collection (T. Kohonen, S. Kaski, K. Lagus, J. Sal`ojrvi, J. Honkela, V. Paatero, A. Saarela). Document classification with Self-Organizing Maps (D. Merkl). Navigation in databases using Self-Organizing Maps (S.A. Shumsky). Self-Organising Maps in computer aided design of electronic circuits (A. Hemani, A. Postula). Modeling self-organization in the visual cortex (R. Miikkulainen, J.A. Bednar, Y. Choe, J. Sirosh). A spatio-temporal memory based on SOMs with activity diffusion (N.R. Euliano, J.C. Principe). Advances in modeling cortical maps (P.G. Morasso, V. Sanguineti, F. Frisone). Topology preservation in Self-Organizing Maps (T. Villmann). Second-order learing in Self-Organizing Maps (R. Der, M. Herrmann). Energy functions for Self-Organizing Maps (T. Heskes). LVQ and single trial EEG classification (G. Pfurtscheller, M. Pregenzer). Self-Organizing Map in categorization of voice qualities (L. Leinonen). Self-Organizing Map in analysis of large-scale industrial systems (O. Simula, J. Ahola, E. Alhoniemi, J. Himberg, J. Vesanto). Keyword index. |
| Bibliography |
Includes bibliographical references and index. |
| Note |
Print version record. |
| Subject |
Neural networks (Computer science)
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Self-organizing maps.
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Neural Networks, Computer |
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Réseaux neuronaux (Informatique)
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Cartes auto-organisatrices.
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COMPUTERS -- Neural Networks.
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Neural networks (Computer science)
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Self-organizing maps
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Neurale netwerken.
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| Added Author |
Oja, Erkki.
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Kaski, Samuel.
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| Other Form: |
Print version: Kohonen maps. 1st ed. Amsterdam ; New York : Elsevier, 1999 044450270X 9780444502704 (DLC) 99035682 (OCoLC)41580577 |
| ISBN |
9780444502704 |
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044450270X |
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9780080535296 (electronic bk.) |
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0080535291 (electronic bk.) |
| Standard No. |
AU@ 000054979246 |
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CHNEW 001005844 |
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DEBBG BV039832079 |
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DEBBG BV042307482 |
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DEBSZ 401487687 |
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NZ1 12432957 |
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NZ1 15190368 |
|
