Neural Network Model Selection Using Asymptotic Jackknife Estimator And Cross Validation Method

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Neural Network Model Selection Using Asymptotic Jackknife Estimator And Cross Validation Method

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Author :
language : en
Publisher:
Release Date : 1993

Neural Network Model Selection Using Asymptotic Jackknife Estimator And Cross Validation Method written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1993 with categories.

Two theorems and a lemma are presented about the use of jackknife estimator and the cross-validation method for model selection. Theorem 1 gives the asymptotic form for the jackknife estimator. Combined with the model selection criterion, this asymptotic form can be used to obtain the fit of a model. The model selection criterion we used is the negative of the average predictive likelihood, the choice of which is based on the idea of the cross- validation method. Lemma 1 provides a formula for further exploration of the asymptotics of the model selection criterion. Theorem 2 given an asymptotic form of the model selection criterion for the regression case, when the parameters optimization criterion has a penalty term. Theorem 2 also proves the asymptotic equivalence of Moody's model selection criterion (Moody, 1992) and the cross- validation method, when the distance measure between response y and regression function takes the form of a squared difference ... Neural networks, Model selection, Jackknife, Cross-validation.

An Information Theoretic Approach To Neural Computing

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Author : Gustavo Deco
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06

An Information Theoretic Approach To Neural Computing written by Gustavo Deco 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 Computers categories.

A detailed formulation of neural networks from the information-theoretic viewpoint. The authors show how this perspective provides new insights into the design theory of neural networks. In particular they demonstrate how these methods may be applied to the topics of supervised and unsupervised learning, including feature extraction, linear and non-linear independent component analysis, and Boltzmann machines. Readers are assumed to have a basic understanding of neural networks, but all the relevant concepts from information theory are carefully introduced and explained. Consequently, readers from varied scientific disciplines, notably cognitive scientists, engineers, physicists, statisticians, and computer scientists, will find this an extremely valuable introduction to this topic.

Feedforward Neural Network Methodology

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Author : Terrence L. Fine
language : en
Publisher: Springer Science & Business Media
Release Date : 2006-04-06

Feedforward Neural Network Methodology written by Terrence L. Fine 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 2006-04-06 with Computers categories.

This decade has seen an explosive growth in computational speed and memory and a rapid enrichment in our understanding of artificial neural networks. These two factors provide systems engineers and statisticians with the ability to build models of physical, economic, and information-based time series and signals. This book provides a thorough and coherent introduction to the mathematical properties of feedforward neural networks and to the intensive methodology which has enabled their highly successful application to complex problems.

Pattern Recognition And Neural Networks

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Author : Brian D. Ripley
language : en
Publisher: Cambridge University Press
Release Date : 1996-01-18

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 1996-01-18 with Computers categories.

This 1996 book explains the statistical framework for pattern recognition and machine learning, now in paperback.

Soft Computing In Systems And Control Technology

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Author : Spyros Tzafestas
language : en
Publisher: World Scientific
Release Date : 1999-05-21

Soft Computing In Systems And Control Technology written by Spyros Tzafestas and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 1999-05-21 with Technology & Engineering categories.

Soft computing is a branch of computing which, unlike hard computing, can deal with uncertain, imprecise and inexact data. The three constituents of soft computing are fuzzy-logic-based computing, neurocomputing, and genetic algorithms. Fuzzy logic contributes the capability of approximate reasoning, neurocomputing offers function approximation and learning capabilities, and genetic algorithms provide a methodology for systematic random search and optimization. These three capabilities are combined in a complementary and synergetic fashion.This book presents a cohesive set of contributions dealing with important issues and applications of soft computing in systems and control technology. The contributions include state-of-the-art material, mathematical developments, fresh results, and how-to-do issues. Among the problems studied via neural, fuzzy, neurofuzzy and genetic methodologies are: data fusion, reinforcement learning, approximation properties, multichannel imaging, signal processing, system optimization, gaming, and several forms of control.The book can serve as a reference for researchers and practitioners in the field. Readers can find in it a large amount of useful and timely information, and thus save considerable effort in searching for other scattered literature.

From Statistics To Neural Networks

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Author : Vladimir Cherkassky
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06

From Statistics To Neural Networks written by Vladimir Cherkassky 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 Computers categories.

The NATO Advanced Study Institute From Statistics to Neural Networks, Theory and Pattern Recognition Applications took place in Les Arcs, Bourg Saint Maurice, France, from June 21 through July 2, 1993. The meeting brought to gether over 100 participants (including 19 invited lecturers) from 20 countries. The invited lecturers whose contributions appear in this volume are: L. Almeida (INESC, Portugal), G. Carpenter (Boston, USA), V. Cherkassky (Minnesota, USA), F. Fogelman Soulie (LRI, France), W. Freeman (Berkeley, USA), J. Friedman (Stanford, USA), F. Girosi (MIT, USA and IRST, Italy), S. Grossberg (Boston, USA), T. Hastie (AT&T, USA), J. Kittler (Surrey, UK), R. Lippmann (MIT Lincoln Lab, USA), J. Moody (OGI, USA), G. Palm (U1m, Germany), B. Ripley (Oxford, UK), R. Tibshirani (Toronto, Canada), H. Wechsler (GMU, USA), C. Wellekens (Eurecom, France) and H. White (San Diego, USA). The ASI consisted of lectures overviewing major aspects of statistical and neural network learning, their links to biological learning and non-linear dynamics (chaos), and real-life examples of pattern recognition applications. As a result of lively interactions between the participants, the following topics emerged as major themes of the meeting: (1) Unified framework for the study of Predictive Learning in Statistics and Artificial Neural Networks (ANNs); (2) Differences and similarities between statistical and ANN methods for non parametric estimation from examples (learning); (3) Fundamental connections between artificial learning systems and biological learning systems.

Scientific And Technical Aerospace Reports

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Author :
language : en
Publisher:
Release Date : 1994

Scientific And Technical Aerospace Reports written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1994 with Aeronautics categories.

Selecting Training Exemplars For Neural Network Learning

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Author : Mark Plutowski
language : en
Publisher:
Release Date : 1994

Selecting Training Exemplars For Neural Network Learning written by Mark Plutowski and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1994 with Machine learning categories.

Computational Learning Theory And Natural Learning Systems Making Learning Systems Practical

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Author : Russell Greiner
language : en
Publisher: MIT Press
Release Date : 1994

Computational Learning Theory And Natural Learning Systems Making Learning Systems Practical written by Russell Greiner and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1994 with Computational learning theory categories.

This is the fourth and final volume of papers from a series of workshops called "Computational Learning Theory and Ǹatural' Learning Systems." The purpose of the workshops was to explore the emerging intersection of theoretical learning research and natural learning systems. The workshops drew researchers from three historically distinct styles of learning research: computational learning theory, neural networks, and machine learning (a subfield of AI). Volume I of the series introduces the general focus of the workshops. Volume II looks at specific areas of interaction between theory and experiment. Volumes III and IV focus on key areas of learning systems that have developed recently. Volume III looks at the problem of "Selecting Good Models." The present volume, Volume IV, looks at ways of "Making Learning Systems Practical." The editors divide the twenty-one contributions into four sections. The first three cover critical problem areas: 1) scaling up from small problems to realistic ones with large input dimensions, 2) increasing efficiency and robustness of learning methods, and 3) developing strategies to obtain good generalization from limited or small data samples. The fourth section discusses examples of real-world learning systems. Contributors : Klaus Abraham-Fuchs, Yasuhiro Akiba, Hussein Almuallim, Arunava Banerjee, Sanjay Bhansali, Alvis Brazma, Gustavo Deco, David Garvin, Zoubin Ghahramani, Mostefa Golea, Russell Greiner, Mehdi T. Harandi, John G. Harris, Haym Hirsh, Michael I. Jordan, Shigeo Kaneda, Marjorie Klenin, Pat Langley, Yong Liu, Patrick M. Murphy, Ralph Neuneier, E.M. Oblow, Dragan Obradovic, Michael J. Pazzani, Barak A. Pearlmutter, Nageswara S.V. Rao, Peter Rayner, Stephanie Sage, Martin F. Schlang, Bernd Schurmann, Dale Schuurmans, Leon Shklar, V. Sundareswaran, Geoffrey Towell, Johann Uebler, Lucia M. Vaina, Takefumi Yamazaki, Anthony M. Zador.

Parallel Problem Solving From Nature Ppsn Iv

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Author : Hans-Michael Voigt
language : en
Publisher: Springer Science & Business Media
Release Date : 1996

Parallel Problem Solving From Nature Ppsn Iv written by Hans-Michael Voigt 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 1996 with Artificial intelligence categories.

This book constitutes the refereed proceedings of the International Conference on Evolutionary Computation held jointly with the 4th Conference on Parallel Problem Solving from Nature, PPSN IV, in Berlin, Germany, in September 1996. The 103 revised papers presented in the volume were carefully selected from more than 160 submissions. The papers are organized in sections on basic concepts of evolutionary computation (EC), theoretical foundations of EC, modifications and extensions of evolutionary algorithms, comparison of methods, other metaphors, and applications of EC in a variety of areas like ML, NNs, engineering, CS, OR, and biology. The book has a comprehensive subject index.