The Radial Basis Function (RBF) neural network has gained in popularity over recent years because of its rapid training and its desirable properties in classification and functional approximation applications. RBF network research has focused on enhanced training algorithms and variations on the basic architecture to improve the performance of the network. In addition, the RBF network is proving to be a valuable tool in a diverse range of application areas, for example, robotics, biomedical engineering, and the financial sector. The two volumes provide a comprehensive survey of the latest developments in this area. Volume 1 covers advances in training algorithms, variations on the architecture and function of the basis neurons, and hybrid paradigms, for example RBF learning using genetic algorithms. Both volumes will prove extremely useful to practitioners in the field, engineers, researchers and technically accomplished managers.
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The Radial Basis Function (RBF) neural network has gained in popularity over recent years because of its rapid training and its desirable properties in classification and functional approximation applications. RBF network research has focused on enhanced training algorithms and variations on the basic architecture to improve the performance of the network. In addition, the RBF network is proving to be a valuable tool in a diverse range of application areas, for example, robotics, biomedical engineering, and the financial sector. The two volumes provide a comprehensive survey of the latest developments in this area. Volume 1 covers advances in training algorithms, variations on the architecture and function of the basis neurons, and hybrid paradigms, for example RBF learning using genetic algorithms. Both volumes will prove extremely useful to practitioners in the field, engineers, researchers and technically accomplished managers.
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Neural Computation, Factor Analysis, New York, Neural Information Processing Systems, San Mateo, International Conference, Morgan Kaufmann, Technical Report, Biological Cybernetics, Complex Systems, Monte Carlo, Support Vector Learning, Cambridge University Press, Cognitive Science, Genetic Algorithms, Artificial Intelligence, John Wiley, Kernel Principal Component Analysis, New Theory, Principal Component Analysis Asteroid, Singular Value Decomposition, Comprehensive Foundation, Information Theory, Proceedings of the European Symposium, Springer Verlag New!
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Neural Computation, Factor Analysis, New York, Neural Information Processing Systems, San Mateo, International Conference, Morgan Kaufmann, Technical Report, Biological Cybernetics, Complex Systems, Monte Carlo, Support Vector Learning, Cambridge University Press, Cognitive Science, Genetic Algorithms, Artificial Intelligence, John Wiley, Kernel Principal Component Analysis, New Theory, Principal Component Analysis Asteroid, Singular Value Decomposition, Comprehensive Foundation, Information Theory, Proceedings of the European Symposium, Springer Verlag New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
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This book cites 61
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- Advances in Kernel Methods: Support Vector Learning by Bernhard Schölkopf on page 50, page 188, and page 190
- Statistical Learning Theory by Vladimir N. Vapnik on page 94, and page 191
- Advances in Neural Information Processing Systems 12 by Sara A. Solla on page 275, and page 277
- Numerical Recipes: The Art of Scientific Computing by William H. Press on page 64, and Back Matter
- Self-organization and associative memory (Springer series in information sciences) by Teuvo Kohonen on page 151, and page 277
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