A Statistical Approach to Neural Networks for Pattern Recognition by Robert A. D

Description: FREE SHIPPING UK WIDE A Statistical Approach to Neural Networks for Pattern Recognition by Robert A. Dunne This book presents a statistical treatment of the Multilayer Perceptron (MLP), which is the most widely used of the neural network models, in a language that is familiar to practicing statisticians. Questions arise when statisticians are first confronted with such a model, and this books aim is to provide thorough answers. FORMAT Hardcover LANGUAGE English CONDITION Brand New Publisher Description An accessible and up-to-date treatment featuring the connection between neural networks and statistics A Statistical Approach to Neural Networks for Pattern Recognition presents a statistical treatment of the Multilayer Perceptron (MLP), which is the most widely used of the neural network models. This book aims to answer questions that arise when statisticians are first confronted with this type of model, such as: How robust is the model to outliers? Could the model be made more robust? Which points will have a high leverage? What are good starting values for the fitting algorithm? Thorough answers to these questions and many more are included, as well as worked examples and selected problems for the reader. Discussions on the use of MLP models with spatial and spectral data are also included. Further treatment of highly important principal aspects of the MLP are provided, such as the robustness of the model in the event of outlying or atypical data; the influence and sensitivity curves of the MLP; why the MLP is a fairly robust model; and modifications to make the MLP more robust. The author also provides clarification of several misconceptions that are prevalent in existing neural network literature. Throughout the book, the MLP model is extended in several directions to show that a statistical modeling approach can make valuable contributions, and further exploration for fitting MLP models is made possible via the R and S-PLUS® codes that are available on the books related Web site. A Statistical Approach to Neural Networks for Pattern Recognition successfully connects logistic regression and linear discriminant analysis, thus making it a critical reference and self-study guide for students and professionals alike in the fields of mathematics, statistics, computer science, and electrical engineering. Back Cover An accessible and up-to-date treatment featuring the connection between neural networks and statistics A Statistical Approach to Neural Networks for Pattern Recognition presents a statistical treatment of the Multilayer Perceptron (MLP), which is the most widely used of the neural network models. This book aims to answer questions that arise when statisticians are first confronted with this type of model, such as: How robust is the model to outliers? Could the model be made more robust? Which points will have a high leverage? What are good starting values for the fitting algorithm? Thorough answers to these questions and many more are included, as well as worked examples and selected problems for the reader. Discussions on the use of MLP models with spatial and spectral data are also included. Further treatment of highly important principal aspects of the MLP are provided, such as the robustness of the model in the event of outlying or atypical data; the influence and sensitivity curves of the MLP; why the MLP is a fairly robust model; and modifications to make the MLP more robust. The author also provides clarification of several misconceptions that are prevalent in existing neural network literature. Throughout the book, the MLP model is extended in several directions to show that a statistical modeling approach can make valuable contributions, and further exploration for fitting MLP models is made possible via the R and S-PLUS® codes that are available on the books related Web site. A Statistical Approach to Neural Networks for Pattern Recognition successfully connects logistic regression and linear discriminant analysis, thus making it a critical reference and self-study guide for students and professionals alike in the fields of mathematics, statistics, computer science, and electrical engineering. Flap An accessible and up-to-date treatment featuring the connection between neural networks and statistics A Statistical Approach to Neural Networks for Pattern Recognition presents a statistical treatment of the Multilayer Perceptron (MLP), which is the most widely used of the neural network models. This book aims to answer questions that arise when statisticians are first confronted with this type of model, such as: How robust is the model to outliers? Could the model be made more robust? Which points will have a high leverage? What are good starting values for the fitting algorithm? Thorough answers to these questions and many more are included, as well as worked examples and selected problems for the reader. Discussions on the use of MLP models with spatial and spectral data are also included. Further treatment of highly important principal aspects of the MLP are provided, such as the robustness of the model in the event of outlying or atypical data; the influence and sensitivity curves of the MLP; why the MLP is a fairly robust model; and modifications to make the MLP more robust. The author also provides clarification of several misconceptions that are prevalent in existing neural network literature. Throughout the book, the MLP model is extended in several directions to show that a statistical modeling approach can make valuable contributions, and further exploration for fitting MLP models is made possible via the R and S-PLUS Author Biography Robert A. Dunne, PhD, is Research Scientist in the Mathematical and Information Sciences Division of the Commonwealth Scientific and Industrial Research Organization (CSIRO) in North Ryde, Australia. Dr. Dunne received his PhD from Murdoch University, and his research interests include remote sensing and bioinformatics. Table of Contents Notation and Code Examples. Preface. Acknowledgments. 1. Introduction. 2. The Multi-Layer Perception Model. 3. Linear Discriminant Analysis. 4. Activation and Penalty Functions. 5. Model Fitting and Evaluation. 6. The Task-Based MLP. 7. Incorporating Spatial Information into an MLP Classifier. 8. Influence Curves for the Multi-Layer Perceptron Classifier. 9. The Sensitivity Curves of the MLP Classifier. 10. A Robust Fitting Procedure for MLP Models. 11. Smoothed Weights. 12. Translation Invariance. 13. Fixed-slope Training. Appendix A. Function Minimization. Appendix B. Maximum Values of the Influence Curve. Topic Index. Review "This book is a good introduction to neural networks for a statistician." (Journal of the American Statistical Association, March 2009) "The book provides an excellent introduction to neutral networks from a statistical perspective." (International Statistical Review, 2008) "Successful connects logistic regression and linear discriminant analysis, thus making it critical reference and self-study guide for students and professionals alike in the fields of mathematics, statistics, computer science, and electrical engineering." (Mathematical Reviews) Long Description An accessible and up-to-date treatment featuring the connection between neural networks and statistics A Statistical Approach to Neural Networks for Pattern Recognition presents a statistical treatment of the Multilayer Perceptron (MLP), which is the most widely used of the neural network models. This book aims to answer questions that arise when statisticians are first confronted with this type of model, such as: How robust is the model to outliers? Could the model be made more robust? Which points will have a high leverage? What are good starting values for the fitting algorithm? Thorough answers to these questions and many more are included, as well as worked examples and selected problems for the reader. Discussions on the use of MLP models with spatial and spectral data are also included. Further treatment of highly important principal aspects of the MLP are provided, such as the robustness of the model in the event of outlying or atypical data; the influence and sensitivity curves of the MLP; why the MLP is a fairly robust model; and modifications to make the MLP more robust. The author also provides clarification of several misconceptions that are prevalent in existing neural network literature. Throughout the book, the MLP model is extended in several directions to show that a statistical modeling approach can make valuable contributions, and further exploration for fitting MLP models is made possible via the R and S-PLUS codes that are available on the books related Web site. A Statistical Approach to Neural Networks for Pattern Recognition successfully connects logistic regression and linear discriminant analysis, thus making it a critical reference and self-study guide for students and professionals alike in the fields of mathematics, statistics, computer science, and electrical engineering. Review Text "This book is a good introduction to neural networks for a statistician." (Journal of the American Statistical Association, March 2009) "The book provides an excellent introduction to neutral networks from a statistical perspective." (International Statistical Review, 2008) "Successful connects logistic regression and linear discriminant analysis, thus making it critical reference and self-study guide for students and professionals alike in the fields of mathematics, statistics, computer science, and electrical engineering." (Mathematical Reviews) Review Quote "This book is a good introduction to neural networks for a statistician." ( Journal of the American Statistical Association , March 2009) "The book provides an excellent introduction to neutral networks from a statistical perspective." ( International Statistical Review , 2008) "Successful connects logistic regression and linear discriminant analysis, thus making it critical reference and self-study guide for students and professionals alike in the fields of mathematics, statistics, computer science, and electrical engineering." ( Mathematical Reviews ) Promotional "Headline" "The book provides an excellent introduction to neutral networks from a statistical perspective." (International Statistical Review, 2008)"Successful connects logistic regression and linear discriminant analysis, thus making it critical reference and self-study guide for students and professionals alike in the fields of mathematics, statistics, computer science, and electrical engineering." (Mathematical Reviews) Feature This book provides the most up-to-date connection between neural networks and statistics. Connects logistic regression and linear discriminant analysis, thus making the book very accessible for statisticians. Provides worked examples and homework problems for the reader. Extends the MLP model in several directions to show that a statistical modeling approach can make valuable contributions. Clarifies several misconceptions that are prevalent in the neural network literature, such as the confusion between the model and the methodology for fitting the model. Provides a treatment of highly important principal aspects such as the robustness of the model in the event of outlying or atypical data. Provides R and S-PLUS code for fitting MLP models via an ftp site. Details ISBN0471741086 Author Robert A. Dunne Short Title STATISTICAL APPROACH TO NEURAL Series Wiley Series in Computational Statistics Language English ISBN-10 0471741086 ISBN-13 9780471741084 Media Book Format Hardcover DEWEY 006.32 Year 2007 Alternative 9780470148150 Edition 1st DOI 10.1604/9780471741084 Series Number 639 AU Release Date 2007-08-07 NZ Release Date 2007-08-07 US Release Date 2007-08-07 UK Release Date 2007-08-07 Place of Publication Chicester Pages 288 Publisher John Wiley & Sons Inc Publication Date 2007-08-07 Imprint Wiley-Interscience Country of Publication United States Illustrations Charts: 1 B&W, 0 Color; Photos: 2 B&W, 0 Color; Drawings: 23 B&W, 0 Color; Maps: 7 B&W, 0 Color; Graphs: 64 B&W, 0 Color Audience Professional & Vocational We've got this At The Nile, if you're looking for it, we've got it. With fast shipping, low prices, friendly service and well over a million items - you're bound to find what you want, at a price you'll love! 30 DAY RETURN POLICY No questions asked, 30 day returns! 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