Artificial Neural Networks And Statistical Pattern Recognition
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Artificial Neural Networks And Statistical Pattern Recognition
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Author : Anil K. Jain
language : en
Publisher:
Release Date : 1996
Artificial Neural Networks And Statistical Pattern Recognition written by Anil K. Jain and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1996 with categories.
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.
Artificial Neural Networks And Statistical Pattern Recognition
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Author : I.K. Sethi
language : en
Publisher: Elsevier
Release Date : 2014-06-28
Artificial Neural Networks And Statistical Pattern Recognition written by I.K. Sethi and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-06-28 with Computers categories.
With the growing complexity of pattern recognition related problems being solved using Artificial Neural Networks, many ANN researchers are grappling with design issues such as the size of the network, the number of training patterns, and performance assessment and bounds. These researchers are continually rediscovering that many learning procedures lack the scaling property; the procedures simply fail, or yield unsatisfactory results when applied to problems of bigger size. Phenomena like these are very familiar to researchers in statistical pattern recognition (SPR), where the curse of dimensionality is a well-known dilemma. Issues related to the training and test sample sizes, feature space dimensionality, and the discriminatory power of different classifier types have all been extensively studied in the SPR literature. It appears however that many ANN researchers looking at pattern recognition problems are not aware of the ties between their field and SPR, and are therefore unable to successfully exploit work that has already been done in SPR. Similarly, many pattern recognition and computer vision researchers do not realize the potential of the ANN approach to solve problems such as feature extraction, segmentation, and object recognition. The present volume is designed as a contribution to the greater interaction between the ANN and SPR research communities.
Introduction To Pattern Recognition
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Author : Menahem Friedman
language : en
Publisher: World Scientific
Release Date : 1999
Introduction To Pattern Recognition written by Menahem Friedman and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 1999 with Computers categories.
This book is an introduction to pattern recognition, meant for undergraduate and graduate students in computer science and related fields in science and technology. Most of the topics are accompanied by detailed algorithms and real world applications. In addition to statistical and structural approaches, novel topics such as fuzzy pattern recognition and pattern recognition via neural networks are also reviewed. Each topic is followed by several examples solved in detail. The only prerequisites for using this book are a one-semester course in discrete mathematics and a knowledge of the basic preliminaries of calculus, linear algebra and probability theory.
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.
Review Of Artificial Neural Networks And Statistical Pattern Recognition By Sethi And Jain
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Author : Ricoh Corporation. California Research Center
language : en
Publisher:
Release Date : 1992
Review Of Artificial Neural Networks And Statistical Pattern Recognition By Sethi And Jain written by Ricoh Corporation. California Research Center and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1992 with categories.
Pattern Recognition With Neural Networks In C
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Author : Abhijit S. Pandya
language : en
Publisher: CRC Press
Release Date : 1995-10-17
Pattern Recognition With Neural Networks In C written by Abhijit S. Pandya and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1995-10-17 with Computers categories.
The addition of artificial neural network computing to traditional pattern recognition has given rise to a new, different, and more powerful methodology that is presented in this interesting book. This is a practical guide to the application of artificial neural networks. Geared toward the practitioner, Pattern Recognition with Neural Networks in C++ covers pattern classification and neural network approaches within the same framework. Through the book's presentation of underlying theory and numerous practical examples, readers gain an understanding that will allow them to make judicious design choices rendering neural application predictable and effective. The book provides an intuitive explanation of each method for each network paradigm. This discussion is supported by a rigorous mathematical approach where necessary. C++ has emerged as a rich and descriptive means by which concepts, models, or algorithms can be precisely described. For many of the neural network models discussed, C++ programs are presented for the actual implementation. Pictorial diagrams and in-depth discussions explain each topic. Necessary derivative steps for the mathematical models are included so that readers can incorporate new ideas into their programs as the field advances with new developments. For each approach, the authors clearly state the known theoretical results, the known tendencies of the approach, and their recommendations for getting the best results from the method. The material covered in the book is accessible to working engineers with little or no explicit background in neural networks. However, the material is presented in sufficient depth so that those with prior knowledge will find this book beneficial. Pattern Recognition with Neural Networks in C++ is also suitable for courses in neural networks at an advanced undergraduate or graduate level. This book is valuable for academic as well as practical research.
Pattern Classification
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Author : Jgen Schmann
language : en
Publisher: Wiley-Interscience
Release Date : 1996-03-15
Pattern Classification written by Jgen Schmann and has been published by Wiley-Interscience this book supported file pdf, txt, epub, kindle and other format this book has been release on 1996-03-15 with Business & Economics categories.
Based on Schurmann's years of practical experience in the area of character recognition and document analysis, this book offers a unifying perspective of neural networks and statistical pattern classification from a theoretically-based engineering point of view. Using graphs and examples, it sheds light on the relation between seemingly different approaches to pattern recognition.
Pattern Recognition Statistical Structural And Neural Approaches
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Author : Schalkoff
language : en
Publisher: John Wiley & Sons
Release Date : 2007-09
Pattern Recognition Statistical Structural And Neural Approaches written by Schalkoff and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-09 with categories.
About The Book: This book explores the heart of pattern recognition concepts, methods and applications using statistical, syntactic and neural approaches. Divided into four sections, it clearly demonstrates the similarities and differences among the three approaches. The second part deals with the statistical pattern recognition approach, starting with a simple example and finishing with unsupervised learning through clustering. Section three discusses the syntactic approach and explores such topics as the capabilities of string grammars and parsing; higher dimensional representations and graphical approaches. Part four presents an excellent overview of the emerging neural approach including an examination of pattern associations and feedforward nets. Along with examples, each chapter provides the reader with pertinent literature for a more in-depth study of specific topics.
Multivariate Statistical Machine Learning Methods For Genomic Prediction
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Author : Osval Antonio Montesinos López
language : en
Publisher: Springer Nature
Release Date : 2022-02-14
Multivariate Statistical Machine Learning Methods For Genomic Prediction written by Osval Antonio Montesinos López and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-02-14 with Technology & Engineering categories.
This book is open access under a CC BY 4.0 license This open access book brings together the latest genome base prediction models currently being used by statisticians, breeders and data scientists. It provides an accessible way to understand the theory behind each statistical learning tool, the required pre-processing, the basics of model building, how to train statistical learning methods, the basic R scripts needed to implement each statistical learning tool, and the output of each tool. To do so, for each tool the book provides background theory, some elements of the R statistical software for its implementation, the conceptual underpinnings, and at least two illustrative examples with data from real-world genomic selection experiments. Lastly, worked-out examples help readers check their own comprehension.The book will greatly appeal to readers in plant (and animal) breeding, geneticists and statisticians, as it provides in a very accessible way the necessary theory, the appropriate R code, and illustrative examples for a complete understanding of each statistical learning tool. In addition, it weighs the advantages and disadvantages of each tool.