Advances In Self Organizing Maps And Learning Vector Quantization

Author : Alfredo Vellido,Karina Gibert,Cecilio Angulo,José David Martín Guerrero
Publisher : Springer
Page : 342 pages
File Size : 52,6 Mb
Release : 2019-04-27
Category : Technology & Engineering
ISBN : 9783030196424

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Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization by Alfredo Vellido,Karina Gibert,Cecilio Angulo,José David Martín Guerrero Pdf

This book gathers papers presented at the 13th International Workshop on Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization (WSOM+), which was held in Barcelona, Spain, from the 26th to the 28th of June 2019. Since being founded in 1997, the conference has showcased the state of the art in unsupervised machine learning methods related to the successful and widely used self-organizing map (SOM) method, and extending its scope to clustering and data visualization. In this installment of the AISC series, the reader will find theoretical research on SOM, LVQ and related methods, as well as numerous applications to problems in fields ranging from business and engineering to the life sciences. Given the scope of its coverage, the book will be of interest to machine learning researchers and practitioners in general and, more specifically, to those looking for the latest developments in unsupervised learning and data visualization.

Author : Thomas Villmann,Frank-Michael Schleif,Marika Kaden,Mandy Lange
Publisher : Springer
Page : 314 pages
File Size : 51,8 Mb
Release : 2014-06-10
Category : Technology & Engineering
ISBN : 9783319076959

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Advances in Self-Organizing Maps and Learning Vector Quantization by Thomas Villmann,Frank-Michael Schleif,Marika Kaden,Mandy Lange Pdf

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 the first WSOM-workshop 1997 in Helsinki this workshop focuses on newest results in the field of supervised and unsupervised vector quantization like self-organizing maps for data mining and data classification. This 10th WSOM brought together more than 50 researchers, experts and practitioners in the beautiful small town Mittweida in Saxony (Germany) nearby the mountains Erzgebirge to discuss new developments in the field of unsupervised self-organizing vector quantization systems and learning vector quantization approaches for classification. The book contains the accepted papers of the workshop after a careful review process as well as summaries of the invited talks. Among these book chapters there are excellent examples of the use of self-organizing maps in agriculture, computer science, data visualization, health systems, economics, engineering, social sciences, text and image analysis and time series analysis. Other chapters present the latest theoretical work on self-organizing maps as well as learning vector quantization methods, such as relating those methods to classical statistical decision methods. All the contribution demonstrate that vector quantization methods cover a large range of application areas including data visualization of high-dimensional complex data, advanced decision making and classification or data clustering and data compression.

Author : Erzsébet Merényi,Michael J. Mendenhall,Patrick O'Driscoll
Publisher : Springer
Page : 370 pages
File Size : 42,5 Mb
Release : 2016-01-07
Category : Technology & Engineering
ISBN : 9783319285184

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Advances in Self-Organizing Maps and Learning Vector Quantization by Erzsébet Merényi,Michael J. Mendenhall,Patrick O'Driscoll Pdf

This book contains the articles from the international conference 11th Workshop on Self-Organizing Maps 2016 (WSOM 2016), held at Rice University in Houston, Texas, 6-8 January 2016. WSOM is a biennial international conference series starting with WSOM'97 in Helsinki, Finland, under the guidance and direction of Professor Tuevo Kohonen (Emeritus Professor, Academy of Finland). WSOM brings together the state-of-the-art theory and applications in Competitive Learning Neural Networks: SOMs, LVQs and related paradigms of unsupervised and supervised vector quantization.The current proceedings present the expert body of knowledge of 93 authors from 15 countries in 31 peer reviewed contributions. It includes papers and abstracts from the WSOM 2016 invited speakers representing leading researchers in the theory and real-world applications of Self-Organizing Maps and Learning Vector Quantization: Professor Marie Cottrell (Universite Paris 1 Pantheon Sorbonne, France), Professor Pablo Estevez (University of Chile and Millennium Instituteof Astrophysics, Chile), and Professor Risto Miikkulainen (University of Texas at Austin, USA). The book comprises a diverse set of theoretical works on Self-Organizing Maps, Neural Gas, Learning Vector Quantization and related topics, and an excellent variety of applications to data visualization, clustering, classification, language processing, robotic control, planning, and to the analysis of astronomical data, brain images, clinical data, time series, and agricultural data.

Author : Jan Faigl,Madalina Olteanu,Jan Drchal
Publisher : Springer Nature
Page : 130 pages
File Size : 47,8 Mb
Release : 2022-08-26
Category : Technology & Engineering
ISBN : 9783031154447

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Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization by Jan Faigl,Madalina Olteanu,Jan Drchal Pdf

In this collection, the reader can find recent advancements in self-organizing maps (SOMs) and learning vector quantization (LVQ), including progressive ideas on exploiting features of parallel computing. The collection is balanced in presenting novel theoretical contributions with applied results in traditional fields of SOMs, such as visualization problems and data analysis. Besides, the collection further includes less traditional deployments in trajectory clustering and recent results on exploiting quantum computation. The presented book is worth interest to data analysis and machine learning researchers and practitioners, specifically those interested in being updated with current developments in unsupervised learning, data visualization, and self-organization.

Author : Jan Faigl,Madalina Olteanu,Jan Drchal
Publisher : Unknown
Page : 0 pages
File Size : 54,7 Mb
Release : 2022
Category : Electronic
ISBN : 3031154452

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Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization by Jan Faigl,Madalina Olteanu,Jan Drchal Pdf

In this collection, the reader can find recent advancements in self-organizing maps (SOMs) and learning vector quantization (LVQ), including progressive ideas on exploiting features of parallel computing. The collection is balanced in presenting novel theoretical contributions with applied results in traditional fields of SOMs, such as visualization problems and data analysis. Besides, the collection further includes less traditional deployments in trajectory clustering and recent results on exploiting quantum computation. The presented book is worth interest to data analysis and machine learning researchers and practitioners, specifically those interested in being updated with current developments in unsupervised learning, data visualization, and self-organization.

Author : Nigel Allinson,Hujun Yin,Lesley Allinson,Jon Slack
Publisher : Springer Science & Business Media
Page : 299 pages
File Size : 55,7 Mb
Release : 2012-12-06
Category : Mathematics
ISBN : 9781447107156

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Advances in Self-Organising Maps by Nigel Allinson,Hujun Yin,Lesley Allinson,Jon Slack Pdf

Author : Pablo A. Estévez,José C. Príncipe,Pablo Zegers
Publisher : Springer Science & Business Media
Page : 371 pages
File Size : 53,5 Mb
Release : 2012-12-14
Category : Technology & Engineering
ISBN : 9783642352300

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Advances in Self-Organizing Maps by Pablo A. Estévez,José C. Príncipe,Pablo Zegers Pdf

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 and visualization purposes. Many SOM applications have been developed in engineering and science, and other fields. This book contains refereed papers presented at the 9th Workshop on Self-Organizing Maps (WSOM 2012) held at the Universidad de Chile, Santiago, Chile, on December 12-14, 2012. The workshop brought together researchers and practitioners in the field of self-organizing systems. Among the book chapters there are excellent examples of the use of SOMs in agriculture, computer science, data visualization, health systems, economics, engineering, social sciences, text and image analysis, and time series analysis. Other chapters present the latest theoretical work on SOMs as well as Learning Vector Quantization (LVQ) methods.

Author : Teuvo Kohonen
Publisher : Springer Science & Business Media
Page : 372 pages
File Size : 50,9 Mb
Release : 2012-12-06
Category : Science
ISBN : 9783642976100

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Self-Organizing Maps by Teuvo Kohonen Pdf

The book we have at hand is the fourth monograph I wrote for Springer Verlag. The previous one named "Self-Organization and Associative Mem ory" (Springer Series in Information Sciences, Volume 8) came out in 1984. Since then the self-organizing neural-network algorithms called SOM and LVQ have become very popular, as can be seen from the many works re viewed in Chap. 9. The new results obtained in the past ten years or so have warranted a new monograph. Over these years I have also answered lots of questions; they have influenced the contents of the present book. I hope it would be of some interest and help to the readers if I now first very briefly describe the various phases that led to my present SOM research, and the reasons underlying each new step. I became interested in neural networks around 1960, but could not in terrupt my graduate studies in physics. After I was appointed Professor of Electronics in 1965, it still took some years to organize teaching at the uni versity. In 1968 - 69 I was on leave at the University of Washington, and D. Gabor had just published his convolution-correlation model of autoasso ciative memory. I noticed immediately that there was something not quite right about it: the capacity was very poor and the inherent noise and crosstalk were intolerable. In 1970 I therefore sugge~ted the auto associative correlation matrix memory model, at the same time as J.A. Anderson and K. Nakano.

Author : Jorma Laaksonen,Timo Honkela
Publisher : Springer Science & Business Media
Page : 380 pages
File Size : 42,6 Mb
Release : 2011-06-03
Category : Computers
ISBN : 9783642215650

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Advances in Self-Organizing Maps by Jorma Laaksonen,Timo Honkela Pdf

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 selected from numerous submissions. The papers are organized in topical sections on plenaries; financial and societal applications; theory and methodology; applications of data mining and analysis; language processing and document analysis; and visualization and image processing.

Author : Udo Seiffert
Publisher : Physica
Page : 289 pages
File Size : 46,6 Mb
Release : 2013-11-11
Category : Computers
ISBN : 9783790818109

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Self-Organizing Neural Networks by Udo Seiffert Pdf

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 community of interna tional researchers. A number of extensions and modifications have been developed during the last two decades. The reason is surely not that the original algorithm was imperfect or inad equate. It is rather the universal applicability and easy handling of the SOM. Com pared to many other network paradigms, only a few parameters need to be arranged and thus also for a beginner the network leads to useful and reliable results. Never theless there is scope for improvements and sophisticated new developments as this book impressively demonstrates. The number of published applications utilizing the SOM appears to be unending. As the title of this book indicates, the reader will benefit from some of the latest the oretical developments and will become acquainted with a number of challenging real-world applications. Our aim in producing this book has been to provide an up to-date treatment of the field of self-organizing neural networks, which will be ac cessible to researchers, practitioners and graduated students from diverse disciplines in academics and industry. We are very grateful to the father of the SOMs, Professor Teuvo Kohonen for sup porting this book and contributing the first chapter.

Author : J.C. Principe,Risto Miikkulainen
Publisher : Springer
Page : 374 pages
File Size : 44,5 Mb
Release : 2009-06-04
Category : Computers
ISBN : 9783642023972

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Advances in Self-Organizing Maps by J.C. Principe,Risto Miikkulainen Pdf

th These proceedings contain refereed papers presented at the 7 WSOM held at the Casa Monica Hotel, St. Augustine, Florida, June 8–10, 2009. We designed the wo- shop to serve as a regular forum for researchers in academia and industry who are interested in the exciting field of self-organizing maps (SOM). The program includes excellent examples of the use of SOM in many areas of social sciences, economics, computational biology, engineering, time series analysis, data visualization and c- puter science as well a vibrant set of theoretical papers that keep pushing the envelope of the original SOM. Our deep appreciation is extended to Teuvo Kohonen and Ping Li for the plenary talks and Amaury Lendasse for the organization of the special sessions. Our sincere thanks go to the members of the Technical Committee and other reviewers for their excellent and timely reviews, and above all to the authors whose contributions made this workshop possible. Special thanks go to Julie Veal for her dedication and hard work in coordinating the many details necessary to put together the program and local arrangements. Jose C. Principe Risto Miikkulainen

Author : Klaus Obermayer,Terrence Joseph Sejnowski
Publisher : MIT Press
Page : 472 pages
File Size : 46,6 Mb
Release : 2001
Category : Neural computers
ISBN : 0262650606

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Self-organizing Map Formation by Klaus Obermayer,Terrence Joseph Sejnowski 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

Author : J.C. Principe,Risto Miikkulainen
Publisher : Springer Science & Business Media
Page : 383 pages
File Size : 54,8 Mb
Release : 2009-05-27
Category : Computers
ISBN : 9783642023965

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Advances in Self-Organizing Maps by J.C. Principe,Risto Miikkulainen Pdf

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 carefully reviewed and selected from numerous submissions. The papers deal with topics in the use of SOM in many areas of social sciences, economics, computational biology, engineering, time series analysis, data visualization and theoretical computer science.

Author : Katrin Amunts,Lucio Grandinetti,Thomas Lippert,Nicolai Petkov
Publisher : Springer
Page : 195 pages
File Size : 48,7 Mb
Release : 2016-12-10
Category : Computers
ISBN : 9783319508627

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Brain-Inspired Computing by Katrin Amunts,Lucio Grandinetti,Thomas Lippert,Nicolai Petkov Pdf

This book constitutes revised selected papers from the Second International Workshop on Brain-Inspired Computing, BrainComp 2015, held in Cetraro, Italy, in July 2015. The 14 papers presented in this volume were carefully reviewed and selected for inclusion in this book. They deal with brain structure and function; computational models and brain-inspired computing methods with practical applications; high performance computing; and visualization for brain simulations.

Author : E. Oja,Samuel Kaski
Publisher : Elsevier
Page : 400 pages
File Size : 41,6 Mb
Release : 1999-07-02
Category : Computers
ISBN : 0080535291

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Kohonen Maps by E. Oja,Samuel Kaski Pdf

The Self-Organizing Map, or Kohonen Map, is one of the most widely used neural network algorithms, with thousands of applications covered in the literature. It was one of the strong underlying factors in the popularity of neural networks starting in the early 80's. Currently this method has been included in a large number of commercial and public domain software packages. In this book, top experts on the SOM method take a look at the state of the art and the future of this computing paradigm. The 30 chapters of this book cover the current status of SOM theory, such as connections of SOM to clustering, classification, probabilistic models, and energy functions. Many applications of the SOM are given, with data mining and exploratory data analysis the central topic, applied to large databases of financial data, medical data, free-form text documents, digital images, speech, and process measurements. Biological models related to the SOM are also discussed.