Pattern Recognition By Self Organizing Neural Networks
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Pattern Recognition By Self Organizing Neural Networks
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Author : Gail A. Carpenter
language : en
Publisher: MIT Press
Release Date : 1991
Pattern Recognition By Self Organizing Neural Networks written by Gail A. Carpenter and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1991 with Computers categories.
Pattern Recognition by Self-Organizing Neural Networks presentsthe most recent advances in an area of research that is becoming vitally important in the fields ofcognitive science, neuroscience, artificial intelligence, and neural networks in general. The 19articles take up developments in competitive learning and computational maps, adaptive resonancetheory, and specialized architectures and biological connections. Introductorysurvey articles provide a framework for understanding the many models involved in various approachesto studying neural networks. These are followed in Part 2 by articles that form the foundation formodels of competitive learning and computational mapping, and recent articles by Kohonen, applyingthem to problems in speech recognition, and by Hecht-Nielsen, applying them to problems in designingadaptive lookup tables. Articles in Part 3 focus on adaptive resonance theory (ART) networks,selforganizing pattern recognition systems whose top-down template feedback signals guarantee theirstable learning in response to arbitrary sequences of input patterns. In Part 4, articles describeembedding ART modules into larger architectures and provide experimental evidence fromneurophysiology, event-related potentials, and psychology that support the prediction that ARTmechanisms exist in the brain. Contributors: J.-P. Banquet, G.A. Carpenter, S.Grossberg, R. Hecht-Nielsen, T. Kohonen, B. Kosko, T.W. Ryan, N.A. Schmajuk, W. Singer, D. Stork, C.von der Malsburg, C.L. Winter.
Soft Computing Approach To Pattern Recognition And Image Processing
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Author : Ashish Ghosh
language : en
Publisher: World Scientific
Release Date : 2002
Soft Computing Approach To Pattern Recognition And Image Processing written by Ashish Ghosh and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002 with Computers categories.
This volume provides a collection of sixteen articles containing review and new material. In a unified way, they describe the recent development of theories and methodologies in pattern recognition, image processing and vision using fuzzy logic, artificial neural networks, genetic algorithms, rough sets and wavelets with significant real life applications.The book details the theory of granular computing and the role of a rough-neuro approach as a way of computing with words and designing intelligent recognition systems. It also demonstrates applications of the soft computing paradigm to case based reasoning, data mining and bio-informatics with a scope for future research.The contributors from around the world present a balanced mixture of current theory, algorithms and applications, making the book an extremely useful resource for students and researchers alike.
Neural Networks For Pattern Recognition
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Author : Albert Nigrin
language : en
Publisher: MIT Press
Release Date : 1993
Neural Networks For Pattern Recognition written by Albert Nigrin and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1993 with Computers categories.
In a simple and accessible way it extends embedding field theory into areas of machine intelligence that have not been clearly dealt with before. Neural Networks for Pattern Recognition takes the pioneering work in artificial neural networks by Stephen Grossberg and his colleagues to a new level. In a simple and accessible way it extends embedding field theory into areas of machine intelligence that have not been clearly dealt with before. Following a tutorial of existing neural networks for pattern classification, Nigrin expands on these networks to present fundamentally new architectures that perform realtime pattern classification of embedded and synonymous patterns and that will aid in tasks such as vision, speech recognition, sensor fusion, and constraint satisfaction. Nigrin presents the new architectures in two stages. First he presents a network called Sonnet 1 that already achieves important properties such as the ability to learn and segment continuously varied input patterns in real time, to process patterns in a context sensitive fashion, and to learn new patterns without degrading existing categories. He then removes simplifications inherent in Sonnet 1 and introduces radically new architectures. These architectures have the power to classify patterns that may have similar meanings but that have different external appearances (synonyms). They also have been designed to represent patterns in a distributed fashion, both in short-term and long-term memory.
Self Organizing Maps
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Author : Teuvo Kohonen
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Self Organizing Maps written by Teuvo Kohonen and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Science categories.
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.
Self Organizing Neural Networks
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Author : Udo Seiffert
language : en
Publisher: Physica
Release Date : 2013-11-11
Self Organizing Neural Networks written by Udo Seiffert and has been published by Physica this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-11-11 with Computers categories.
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.
Neural Networks For Applied Sciences And Engineering
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Author : Sandhya Samarasinghe
language : en
Publisher: CRC Press
Release Date : 2016-04-19
Neural Networks For Applied Sciences And Engineering written by Sandhya Samarasinghe and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-04-19 with Computers categories.
In response to the exponentially increasing need to analyze vast amounts of data, Neural Networks for Applied Sciences and Engineering: From Fundamentals to Complex Pattern Recognition provides scientists with a simple but systematic introduction to neural networks. Beginning with an introductory discussion on the role of neural networks in
Granular Neural Networks Pattern Recognition And Bioinformatics
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Author : Sankar K. Pal
language : en
Publisher: Springer
Release Date : 2017-05-02
Granular Neural Networks Pattern Recognition And Bioinformatics written by Sankar K. Pal and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-05-02 with Technology & Engineering categories.
This book provides a uniform framework describing how fuzzy rough granular neural network technologies can be formulated and used in building efficient pattern recognition and mining models. It also discusses the formation of granules in the notion of both fuzzy and rough sets. Judicious integration in forming fuzzy-rough information granules based on lower approximate regions enables the network to determine the exactness in class shape as well as to handle the uncertainties arising from overlapping regions, resulting in efficient and speedy learning with enhanced performance. Layered network and self-organizing analysis maps, which have a strong potential in big data, are considered as basic modules,. The book is structured according to the major phases of a pattern recognition system (e.g., classification, clustering, and feature selection) with a balanced mixture of theory, algorithm, and application. It covers the latest findings as well as directions for future research, particularly highlighting bioinformatics applications. The book is recommended for both students and practitioners working in computer science, electrical engineering, data science, system design, pattern recognition, image analysis, neural computing, social network analysis, big data analytics, computational biology and soft computing.
Pattern Recognition And Neural Networks
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Author : Brian D. Ripley
language : en
Publisher: Cambridge University Press
Release Date : 2007
Pattern Recognition And Neural Networks written by Brian D. Ripley and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with Computers categories.
This 1996 book explains the statistical framework for pattern recognition and machine learning, now in paperback.
Multi Resolution Self Organizing Neural Networks For Pattern Recognition
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Author : Penny Pei Chen
language : en
Publisher:
Release Date : 2001
Multi Resolution Self Organizing Neural Networks For Pattern Recognition written by Penny Pei Chen and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001 with categories.
The MRF-ART neural network employs fast competitive learning and efficient parallel matching to solve complex data classification problems. The architecture of the MRF-ART not only preserves the ART-type neural network's characteristics but also extends its capability to represent input patterns in a hierarchical fashion. To achieve this, the MRF-ART network uses multiple output layers arranged in a cascaded manner which is completely different from a conventional fuzzy ART network with only one output layer. Moreover, the parallel matching process makes the MRF-ART network suitable for hardware implementation.
Handbook Of Pattern Recognition And Computer Vision
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Author : C. H. Chen
language : en
Publisher: World Scientific
Release Date : 1993-08
Handbook Of Pattern Recognition And Computer Vision written by C. H. Chen and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 1993-08 with Computers categories.
"The book provides an up-to-date and authoritative treatment of pattern recognition and computer vision, with chapters written by leaders in the field. On the basic methods in pattern recognition and computer vision, topics range from statistical pattern recognition to array grammars to projective geometry to skeletonization, and shape and texture measures."--BOOK JACKET.