Self-Organizing Neural Networks Recent Advances and Applications
The Self-Organizing Map (SOM) is one of the most frequently used architectures for unsupervised artificial neural networks. Introduced by Teuvo Kohonen in the 1980s, SOMs have been developed as a very powerful method for visualization and unsupervised classification tasks by an active and innovative...
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| Format: | eBook |
| Language: | English |
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Heidelberg
Physica
2002, 2002
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| Edition: | 1st ed. 2002 |
| Series: | Studies in Fuzziness and Soft Computing
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| Subjects: | |
| Online Access: | |
| Collection: | Springer Book Archives -2004 - Collection details see MPG.ReNa |
Table of Contents:
- Overture
- Measures for the Organization of Self-Organizing Maps
- Unsupervised Learning and Self-Organization in Networks of Spiking Neurons
- Generative Probability Density Model in the Self-Organizing Map
- Growing Multi-Dimensional Self-Organizing Maps for Motion Detection
- Extensions and Modifications of the Kohonen-SOM and Applications in Remote Sensing Image Analysis
- Modeling Speech Processing and Recognition in the Auditory System Using the Multilevel Hypermap Architecture
- Algorithms for the Visualization of Large and Multivariate Data Sets
- Self-Organizing Maps and Financial Forecasting: an Application
- Unsupervised and Supervised Learning in Radial-Basis-Function Networks
- Parallel Implementations of Self-Organizing Maps