This book provides an overview of self-organizing map formation, including recent developments. Self-organizing maps form a branch of unsupervised learning, which is the study of what can be determined about the statistical properties of input data without explicit feedback from a teacher. The articles are drawn from the journal Neural Computation.The book consists of five sections. The first section looks at attempts to model the organization of cortical maps and at the theory and applications of the related artificial neural network algorithms. The second section analyzes topographic maps and their formation via objective functions. The third section discusses cortical maps of stimulus features. The fourth section discusses self-organizing maps for unsupervised data analysis. The fifth section discusses extensions of self-organizing maps, including two surprising applications of mapping algorithms to standard computer science problems: combinatorial optimization and sorting.Contributors J. J. Atick, H. G. Barrow, H. U. Bauer, C. M. Bishop, H. J. Bray, J. Bruske, J. M. L. Budd, M. Budinich, V. Cherkassky, J. Cowan, R. Durbin, E. Erwin, G. J. Goodhill, T. Graepel, D. Grier, S. Kaski, T. Kohonen, H. Lappalainen, Z. Li, J. Lin, R. Linsker, S. P. Luttrell, D. J. C. MacKay, K. D. Miller, G. Mitchison, F. Mulier, K. Obermayer, C. Piepenbrock, H. Ritter, K. Schulten, T. J. Sejnowski, S. Smirnakis, G. Sommer, M. Svensen, R. Szeliski, A. Utsugi, C. K. I. Williams, L. Wiskott, L. Xu, A. Yuille, J. Zhang.
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Klaus Obermayer is Professor of Computer Science and head of the Neural Information Processing Group at the Technical University of Berlin.
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Linsker (1986, 1988) has studied by simulation the evolution of weight vectors under a Hebb-type teacherless learning rule in a feedforward linear network. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
multiplicative normalization rule, encoding prescription, effective lateral connections, pinwheel model, neural map formation, macaque maps, presynaptic normalization, independent component axes, constrained optimization framework, disjoint receptive fields, generalized deformable models, linear correlation model, elastic net algorithm, lateral connection structure, global orthogonality, ocular dominance bands, elastic net approach, minimal wiring, noisy spiral, dynamic link matching, local orthogonality, hyperparameter selection, perfectly topology preserving map, ocular dominance maps, blob model Key Phrases - Capitalized Phrases (CAPs): (learn more)
New York, San Mateo, Morgan Kaufmann, San Diego, Joint Conf, Brain Res, Graeme Mitchison, University of California, Van Essen, Ralph Linsker, San Francisco, Biological Cybernetics, Harvard University, Lawrence Erlbaum, Neuronale Netze, Physical Review, Technical University of Berlin, Cambridge University Press, Classification Results, Proceedings of the International Conference, Royal Soc, Stanford University, University of Illinois Press, University of Sussex New!
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Linsker (1986, 1988) has studied by simulation the evolution of weight vectors under a Hebb-type teacherless learning rule in a feedforward linear network. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
multiplicative normalization rule, encoding prescription, effective lateral connections, pinwheel model, neural map formation, macaque maps, presynaptic normalization, independent component axes, constrained optimization framework, disjoint receptive fields, generalized deformable models, linear correlation model, elastic net algorithm, lateral connection structure, global orthogonality, ocular dominance bands, elastic net approach, minimal wiring, noisy spiral, dynamic link matching, local orthogonality, hyperparameter selection, perfectly topology preserving map, ocular dominance maps, blob model Key Phrases - Capitalized Phrases (CAPs): (learn more)
New York, San Mateo, Morgan Kaufmann, San Diego, Joint Conf, Brain Res, Graeme Mitchison, University of California, Van Essen, Ralph Linsker, San Francisco, Biological Cybernetics, Harvard University, Lawrence Erlbaum, Neuronale Netze, Physical Review, Technical University of Berlin, Cambridge University Press, Classification Results, Proceedings of the International Conference, Royal Soc, Stanford University, University of Illinois Press, University of Sussex New!
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Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Citations (learn more)
This book cites 28
books:
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3
books
cite this book:
- Self-Organizing Maps by Teuvo Kohonen on 6 pages
- Computation and Neural Systems by Frank H. Eeckman on page 180, page 182, and page 199
- Self-organization and associative memory (Springer series in information sciences) by Teuvo Kohonen on page 181, page 198, and page 245
- The Computational Brain (Computational Neuroscience) by Patricia Smith Churchland in Front Matter (1), and Front Matter (2)
- The Mathematical Theory of Communication by Claude E Shannon on page 38, and page 138
See all 28 books this book cites
- Hands-on AI with Java : Smart Gaming, Robots, and More by Edwin Wise in Back Matter
- Graphical Models: Foundations of Neural Computation (Computational Neuroscience) by Michael I. Jordan in Front Matter
- Unsupervised Learning: Foundations of Neural Computation (Computational Neuroscience) by Geoffrey Hinton in Front Matter
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