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Contributions | Klaus Obermayer (Editor) Terrence J. Sejnowski (Editor) |

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Edition Name | 1st edition |

First Sentence | Linsker (1986, 1988) has studied by simulation the evolution of weight vectors under a Hebb-type teacherless learning rule in a feedforward linear network. |

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Isbn10 | 0262650606 |

Isbn13 | 9780262650601 |

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Languages | /languages/eng |

Latest Revision | 5 |

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Number Of Pages | 415 |

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Physical Dimensions | 8.7 x 5.9 x 0.8 inches |

Physical Format | Paperback |

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Publish Date | October 1, 2001 |

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Publishers | The MIT Press |

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Revision | 5 |

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Subjects | General Theory of Computing Neural Networks Neurosciences Mathematical Statistics Neural Computing Computers Computers - General Information Computer Books: General General Neuroscience Medical / Neuroscience Probability & Statistics - General Neural computers Neural networks (Computer scie Neural networks (Computer science) Self-organizing maps |

Subtitle | Foundations of Neural Computation (Computational Neuroscience) |

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Title | Self-Organizing Map Formation |

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Weight | 1.4 pounds |

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Authors | Klaus Obermayer Terrence Joseph Sejnowski |

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Categories | Computers |

Content Version | 0.1.1.0.preview.1 |

Description | 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. |

Language | en |

Maturity Rating | NOT_MATURE |

Page Count | 440 |

Print Type | BOOK |

Published Date | 2001 |

Publisher | MIT Press |

Ratings Count | - |

Subtitle | Foundations of Neural Computation |

Title | Self-organizing Map Formation |

ISBN_10 | 0262650606 |

ISBN_13 | 9780262650601 |