From Statistics To Neural Networks

Author : Ke-Lin Du,M. N. S. Swamy
Publisher : Springer Science & Business Media
Page : 834 pages
File Size : 54,8 Mb
Release : 2013-12-09
Category : Technology & Engineering
ISBN : 9781447155713

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Neural Networks and Statistical Learning by Ke-Lin Du,M. N. S. Swamy Pdf

Providing a broad but in-depth introduction to neural network and machine learning in a statistical framework, this book provides a single, comprehensive resource for study and further research. All the major popular neural network models and statistical learning approaches are covered with examples and exercises in every chapter to develop a practical working understanding of the content. Each of the twenty-five chapters includes state-of-the-art descriptions and important research results on the respective topics. The broad coverage includes the multilayer perceptron, the Hopfield network, associative memory models, clustering models and algorithms, the radial basis function network, recurrent neural networks, principal component analysis, nonnegative matrix factorization, independent component analysis, discriminant analysis, support vector machines, kernel methods, reinforcement learning, probabilistic and Bayesian networks, data fusion and ensemble learning, fuzzy sets and logic, neurofuzzy models, hardware implementations, and some machine learning topics. Applications to biometric/bioinformatics and data mining are also included. Focusing on the prominent accomplishments and their practical aspects, academic and technical staff, graduate students and researchers will find that this provides a solid foundation and encompassing reference for the fields of neural networks, pattern recognition, signal processing, machine learning, computational intelligence, and data mining.

Author : Vladimir Cherkassky,Jerome H. Friedman,Harry Wechsler
Publisher : Springer Science & Business Media
Page : 414 pages
File Size : 51,9 Mb
Release : 2012-12-06
Category : Computers
ISBN : 9783642791192

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From Statistics to Neural Networks by Vladimir Cherkassky,Jerome H. Friedman,Harry Wechsler Pdf

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.

Author : Jim W. Kay,D. M. Titterington
Publisher : Oxford University Press, USA
Page : 290 pages
File Size : 50,9 Mb
Release : 1999
Category : Computers
ISBN : 0198524226

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Statistics and Neural Networks by Jim W. Kay,D. M. Titterington Pdf

Providing a broad overview of important current developments in the area of neural networks, this book highlights likely future trends.

Author : Basilio de Braganca Pereira,Calyampudi Radhakrishna Rao,Fabio Borges de Oliveira
Publisher : CRC Press
Page : 234 pages
File Size : 54,7 Mb
Release : 2020-09-01
Category : Business & Economics
ISBN : 9780429775550

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Statistical Learning Using Neural Networks by Basilio de Braganca Pereira,Calyampudi Radhakrishna Rao,Fabio Borges de Oliveira Pdf

Statistical Learning using Neural Networks: A Guide for Statisticians and Data Scientists with Python introduces artificial neural networks starting from the basics and increasingly demanding more effort from readers, who can learn the theory and its applications in statistical methods with concrete Python code examples. It presents a wide range of widely used statistical methodologies, applied in several research areas with Python code examples, which are available online. It is suitable for scientists and developers as well as graduate students. Key Features: Discusses applications in several research areas Covers a wide range of widely used statistical methodologies Includes Python code examples Gives numerous neural network models This book covers fundamental concepts on Neural Networks including Multivariate Statistics Neural Networks, Regression Neural Network Models, Survival Analysis Networks, Time Series Forecasting Networks, Control Chart Networks, and Statistical Inference Results. This book is suitable for both teaching and research. It introduces neural networks and is a guide for outsiders of academia working in data mining and artificial intelligence (AI). This book brings together data analysis from statistics to computer science using neural networks.

Author : Haiping Huang
Publisher : Springer Nature
Page : 302 pages
File Size : 40,8 Mb
Release : 2022-01-04
Category : Science
ISBN : 9789811675706

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Statistical Mechanics of Neural Networks by Haiping Huang Pdf

This book highlights a comprehensive introduction to the fundamental statistical mechanics underneath the inner workings of neural networks. The book discusses in details important concepts and techniques including the cavity method, the mean-field theory, replica techniques, the Nishimori condition, variational methods, the dynamical mean-field theory, unsupervised learning, associative memory models, perceptron models, the chaos theory of recurrent neural networks, and eigen-spectrums of neural networks, walking new learners through the theories and must-have skillsets to understand and use neural networks. The book focuses on quantitative frameworks of neural network models where the underlying mechanisms can be precisely isolated by physics of mathematical beauty and theoretical predictions. It is a good reference for students, researchers, and practitioners in the area of neural networks.

Author : Moritz Helias,David Dahmen
Publisher : Springer Nature
Page : 203 pages
File Size : 50,9 Mb
Release : 2020-08-20
Category : Science
ISBN : 9783030464448

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Statistical Field Theory for Neural Networks by Moritz Helias,David Dahmen Pdf

This book presents a self-contained introduction to techniques from field theory applied to stochastic and collective dynamics in neuronal networks. These powerful analytical techniques, which are well established in other fields of physics, are the basis of current developments and offer solutions to pressing open problems in theoretical neuroscience and also machine learning. They enable a systematic and quantitative understanding of the dynamics in recurrent and stochastic neuronal networks. This book is intended for physicists, mathematicians, and computer scientists and it is designed for self-study by researchers who want to enter the field or as the main text for a one semester course at advanced undergraduate or graduate level. The theoretical concepts presented in this book are systematically developed from the very beginning, which only requires basic knowledge of analysis and linear algebra.

Author : Murray Smith
Publisher : Van Nostrand Reinhold Company
Page : 268 pages
File Size : 54,9 Mb
Release : 1993
Category : Computers
ISBN : STANFORD:36105017638508

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Neural Networks for Statistical Modeling by Murray Smith Pdf

Author : Herbert K. H. Lee
Publisher : SIAM
Page : 106 pages
File Size : 50,9 Mb
Release : 2004-01-01
Category : Mathematics
ISBN : 0898718422

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Bayesian Nonparametrics via Neural Networks by Herbert K. H. Lee Pdf

Bayesian Nonparametrics via Neural Networks is the first book to focus on neural networks in the context of nonparametric regression and classification, working within the Bayesian paradigm. Its goal is to demystify neural networks, putting them firmly in a statistical context rather than treating them as a black box. This approach is in contrast to existing books, which tend to treat neural networks as a machine learning algorithm instead of a statistical model. Once this underlying statistical model is recognized, other standard statistical techniques can be applied to improve the model. The Bayesian approach allows better accounting for uncertainty. This book covers uncertainty in model choice and methods to deal with this issue, exploring a number of ideas from statistics and machine learning. A detailed discussion on the choice of prior and new noninformative priors is included, along with a substantial literature review. Written for statisticians using statistical terminology, Bayesian Nonparametrics via Neural Networks will lead statisticians to an increased understanding of the neural network model and its applicability to real-world problems.

Author : Robert A. Dunne
Publisher : John Wiley & Sons
Page : 289 pages
File Size : 40,9 Mb
Release : 2007-07-20
Category : Mathematics
ISBN : 9780470148143

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

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 book's 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.

Author : P. S. Neelakanta,Dolores DeGroff
Publisher : CRC Press
Page : 194 pages
File Size : 51,8 Mb
Release : 2018-02-06
Category : Technology & Engineering
ISBN : 9781351428958

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Neural Network Modeling by P. S. Neelakanta,Dolores DeGroff Pdf

Neural Network Modeling offers a cohesive approach to the statistical mechanics and principles of cybernetics as a basis for neural network modeling. It brings together neurobiologists and the engineers who design intelligent automata to understand the physics of collective behavior pertinent to neural elements and the self-control aspects of neurocybernetics. The theoretical perspectives and explanatory projections portray the most current information in the field, some of which counters certain conventional concepts in the visualization of neuronal interactions.

Author : Ke-Lin Du,M. N. S. Swamy
Publisher : Springer Nature
Page : 988 pages
File Size : 41,7 Mb
Release : 2019-09-12
Category : Mathematics
ISBN : 9781447174523

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Neural Networks and Statistical Learning by Ke-Lin Du,M. N. S. Swamy Pdf

This book provides a broad yet detailed introduction to neural networks and machine learning in a statistical framework. A single, comprehensive resource for study and further research, it explores the major popular neural network models and statistical learning approaches with examples and exercises and allows readers to gain a practical working understanding of the content. This updated new edition presents recently published results and includes six new chapters that correspond to the recent advances in computational learning theory, sparse coding, deep learning, big data and cloud computing. Each chapter features state-of-the-art descriptions and significant research findings. The topics covered include: • multilayer perceptron; • the Hopfield network; • associative memory models;• clustering models and algorithms; • t he radial basis function network; • recurrent neural networks; • nonnegative matrix factorization; • independent component analysis; •probabilistic and Bayesian networks; and • fuzzy sets and logic. Focusing on the prominent accomplishments and their practical aspects, this book provides academic and technical staff, as well as graduate students and researchers with a solid foundation and comprehensive reference on the fields of neural networks, pattern recognition, signal processing, and machine learning.

Author : Brian D. Ripley
Publisher : Cambridge University Press
Page : 420 pages
File Size : 46,6 Mb
Release : 2007
Category : Computers
ISBN : 0521717701

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Pattern Recognition and Neural Networks by Brian D. Ripley Pdf

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

Author : Radford M. Neal
Publisher : Springer Science & Business Media
Page : 194 pages
File Size : 48,8 Mb
Release : 2012-12-06
Category : Mathematics
ISBN : 9781461207450

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Bayesian Learning for Neural Networks by Radford M. Neal Pdf

Artificial "neural networks" are widely used as flexible models for classification and regression applications, but questions remain about how the power of these models can be safely exploited when training data is limited. This book demonstrates how Bayesian methods allow complex neural network models to be used without fear of the "overfitting" that can occur with traditional training methods. Insight into the nature of these complex Bayesian models is provided by a theoretical investigation of the priors over functions that underlie them. A practical implementation of Bayesian neural network learning using Markov chain Monte Carlo methods is also described, and software for it is freely available over the Internet. Presupposing only basic knowledge of probability and statistics, this book should be of interest to researchers in statistics, engineering, and artificial intelligence.

Author : Raul Rojas
Publisher : Springer Science & Business Media
Page : 511 pages
File Size : 47,7 Mb
Release : 2013-06-29
Category : Computers
ISBN : 9783642610684

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Neural Networks by Raul Rojas Pdf

Neural networks are a computing paradigm that is finding increasing attention among computer scientists. In this book, theoretical laws and models previously scattered in the literature are brought together into a general theory of artificial neural nets. Always with a view to biology and starting with the simplest nets, it is shown how the properties of models change when more general computing elements and net topologies are introduced. Each chapter contains examples, numerous illustrations, and a bibliography. The book is aimed at readers who seek an overview of the field or who wish to deepen their knowledge. It is suitable as a basis for university courses in neurocomputing.

Author : Terrence L. Fine
Publisher : Springer Science & Business Media
Page : 340 pages
File Size : 46,6 Mb
Release : 2006-04-06
Category : Computers
ISBN : 9780387226491

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Feedforward Neural Network Methodology by Terrence L. Fine Pdf

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.