An Introduction To Biological And Artificial Neural Networks For Pattern Recognition
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An Introduction To Biological And Artificial Neural Networks For Pattern Recognition
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Author : Steven K. Rogers
language : en
Publisher: SPIE Press
Release Date : 1991
An Introduction To Biological And Artificial Neural Networks For Pattern Recognition written by Steven K. Rogers and has been published by SPIE Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1991 with Computers categories.
An Introduction To Neural Computing
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Author : Igor Aleksander
language : en
Publisher: Van Nostrand Reinhold Company
Release Date : 1990
An Introduction To Neural Computing written by Igor Aleksander and has been published by Van Nostrand Reinhold Company this book supported file pdf, txt, epub, kindle and other format this book has been release on 1990 with Computers categories.
The second edition of this text has been updated and includes material on new developments including neurocontrol, pattern analysis and dynamic systems. The book should be useful for undergraduate students of neural networks.
Artificial Neural Networks
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Author : Kevin L. Priddy
language : en
Publisher: SPIE Press
Release Date : 2005
Artificial Neural Networks written by Kevin L. Priddy and has been published by SPIE Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with Computers categories.
This tutorial text provides the reader with an understanding of artificial neural networks (ANNs), and their application, beginning with the biological systems which inspired them, through the learning methods that have been developed, and the data collection processes, to the many ways ANNs are being used today. The material is presented with a minimum of math (although the mathematical details are included in the appendices for interested readers), and with a maximum of hands-on experience. All specialized terms are included in a glossary. The result is a highly readable text that will teach the engineer the guiding principles necessary to use and apply artificial neural networks.
An Introduction To Biological And Artificial Neural Networks For Pattern Recognition
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Author : Steven K. Rogers
language : en
Publisher:
Release Date : 1991
An Introduction To Biological And Artificial Neural Networks For Pattern Recognition written by Steven K. Rogers and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1991 with Neural networks (Computer science) categories.
Artificial Neural Network Modelling
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Author : Subana Shanmuganathan
language : en
Publisher: Springer
Release Date : 2016-02-03
Artificial Neural Network Modelling written by Subana Shanmuganathan and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-02-03 with Technology & Engineering categories.
This book covers theoretical aspects as well as recent innovative applications of Artificial Neural networks (ANNs) in natural, environmental, biological, social, industrial and automated systems. It presents recent results of ANNs in modelling small, large and complex systems under three categories, namely, 1) Networks, Structure Optimisation, Robustness and Stochasticity 2) Advances in Modelling Biological and Environmental Systems and 3) Advances in Modelling Social and Economic Systems. The book aims at serving undergraduates, postgraduates and researchers in ANN computational modelling.
Data Analytics In Bioinformatics
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Author : Rabinarayan Satpathy
language : en
Publisher: John Wiley & Sons
Release Date : 2021-01-20
Data Analytics In Bioinformatics written by Rabinarayan Satpathy 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 2021-01-20 with Computers categories.
Machine learning techniques are increasingly being used to address problems in computational biology and bioinformatics. Novel machine learning computational techniques to analyze high throughput data in the form of sequences, gene and protein expressions, pathways, and images are becoming vital for understanding diseases and future drug discovery. Machine learning techniques such as Markov models, support vector machines, neural networks, and graphical models have been successful in analyzing life science data because of their capabilities in handling randomness and uncertainty of data noise and in generalization. Machine Learning in Bioinformatics compiles recent approaches in machine learning methods and their applications in addressing contemporary problems in bioinformatics approximating classification and prediction of disease, feature selection, dimensionality reduction, gene selection and classification of microarray data and many more.
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.
Adaptive Pattern Recognition And Neural Networks
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Author : Yoh-Han Pao
language : en
Publisher: Addison Wesley Publishing Company
Release Date : 1989
Adaptive Pattern Recognition And Neural Networks written by Yoh-Han Pao and has been published by Addison Wesley Publishing Company this book supported file pdf, txt, epub, kindle and other format this book has been release on 1989 with Computers categories.
A coherent introduction to the basic concepts of pattern recognition, incorporating recent advances from AI, neurobiology, engineering, and other disciplines. Treats specifically the implementation of adaptive pattern recognition to neural networks. Annotation copyright Book News, Inc. Portland, Or.
An Introduction To Neural Networks
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Author : Kevin Gurney
language : en
Publisher: CRC Press
Release Date : 2018-10-08
An Introduction To Neural Networks written by Kevin Gurney and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-10-08 with Computers categories.
Though mathematical ideas underpin the study of neural networks, the author presents the fundamentals without the full mathematical apparatus. All aspects of the field are tackled, including artificial neurons as models of their real counterparts; the geometry of network action in pattern space; gradient descent methods, including back-propagation; associative memory and Hopfield nets; and self-organization and feature maps. The traditionally difficult topic of adaptive resonance theory is clarified within a hierarchical description of its operation. The book also includes several real-world examples to provide a concrete focus. This should enhance its appeal to those involved in the design, construction and management of networks in commercial environments and who wish to improve their understanding of network simulator packages. As a comprehensive and highly accessible introduction to one of the most important topics in cognitive and computer science, this volume should interest a wide range of readers, both students and professionals, in cognitive science, psychology, computer science and electrical engineering.
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.