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Download the fantastic book titled Self organizing Map Formation written by Klaus Obermayer, available in its entirety in both PDF and EPUB formats for online reading. This page includes a concise summary, a preview of the book cover, and detailed information about "Self organizing Map Formation", which was released on 17 June 2024. We suggest perusing the summary before initiating your download. This book is a top selection for enthusiasts of the Neural computers genre.

Summary of Self organizing Map Formation by Klaus Obermayer PDF

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

Self organizing Map Formation

Detail About Self organizing Map Formation PDF

  • Author : Klaus Obermayer
  • Publisher : MIT Press
  • Genre : Neural computers
  • Total Pages : 472 pages
  • ISBN : 9780262650601
  • PDF File Size : 32,8 Mb
  • Language : English
  • Rating : 4/5 from 21 reviews

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Self-organizing Map Formation

Self-organizing Map Formation
  • Publisher : MIT Press
  • File Size : 30,6 Mb
  • Release Date : 17 June 2024
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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

Computational Intelligence Systems in Industrial Engineering

Computational Intelligence Systems in Industrial Engineering
  • Publisher : Springer Science & Business Media
  • File Size : 38,7 Mb
  • Release Date : 05 November 2012
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Industrial engineering is a branch of engineering dealing with the optimization of complex processes or systems. It is concerned with the development, improvement, implementation and evaluation of production and service

Advances in Self-Organizing Maps

Advances in Self-Organizing Maps
  • Publisher : Springer Science & Business Media
  • File Size : 27,8 Mb
  • Release Date : 27 May 2009
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This book constitutes the refereed proceedings of the 7th International Workshop on Advances in Self-Organizing Maps, WSOM 2009, held in St. Augustine, Florida, in June 2009. The 41 revised full papers presented were

Handbook of Natural Computing

Handbook of Natural Computing
  • Publisher : Springer
  • File Size : 38,7 Mb
  • Release Date : 09 July 2012
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Natural Computing is the field of research that investigates both human-designed computing inspired by nature and computing taking place in nature, i.e., it investigates models and computational techniques inspired

Advances in Self-Organizing Maps

Advances in Self-Organizing Maps
  • Publisher : Springer Science & Business Media
  • File Size : 20,7 Mb
  • Release Date : 03 June 2011
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This book constitutes the refereed proceedings of the 8th International Workshop on Self-Organizing Maps, WSOM 2011, held in Espoo, Finland, in June 2011. The 36 revised full papers presented were carefully reviewed and

Self-Organizing Maps

Self-Organizing Maps
  • Publisher : Springer Science & Business Media
  • File Size : 51,9 Mb
  • Release Date : 06 December 2012
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The second, revised edition of this book was suggested by the impressive sales of the first edition. Fortunately this enabled us to incorporate new important results that had just been

Applications of Self-Organizing Maps

Applications of Self-Organizing Maps
  • Publisher : BoD – Books on Demand
  • File Size : 45,7 Mb
  • Release Date : 21 November 2012
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The self-organizing map, first described by the Finnish scientist Teuvo Kohonen, can by applied to a wide range of fields. This book is about such applications, i.e. how the

Advances in Self-Organizing Maps and Learning Vector Quantization

Advances in Self-Organizing Maps and Learning Vector Quantization
  • Publisher : Springer
  • File Size : 22,6 Mb
  • Release Date : 10 June 2014
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The book collects the scientific contributions presented at the 10th Workshop on Self-Organizing Maps (WSOM 2014) held at the University of Applied Sciences Mittweida, Mittweida (Germany, Saxony), on July 2–4, 2014. Starting with

Faithful Representations and Topographic Maps

Faithful Representations and Topographic Maps
  • Publisher : Wiley-Interscience
  • File Size : 37,7 Mb
  • Release Date : 01 February 2000
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A new perspective on topographic map formation and the advantages of information-based learning The study of topographic map formation provides us with important tools for both biological modeling and statistical

Advances in Self-Organizing Maps

Advances in Self-Organizing Maps
  • Publisher : Springer Science & Business Media
  • File Size : 37,5 Mb
  • Release Date : 14 December 2012
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Self-organizing maps (SOMs) were developed by Teuvo Kohonen in the early eighties. Since then more than 10,000 works have been based on SOMs. SOMs are unsupervised neural networks useful for clustering