From Statistics To Neural Networks

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Neural Networks and Statistical Learning

Author : Ke-Lin Du
Publisher : Springer Science & Business Media
ISBN 13 : 1447155718
Total Pages : 834 pages
Book Rating : 4.4/5 (471 download)

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Book Synopsis Neural Networks and Statistical Learning by : Ke-Lin Du

Download or read book Neural Networks and Statistical Learning written by Ke-Lin Du and published by Springer Science & Business Media. This book was released on 2013-12-09 with total page 834 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

From Statistics to Neural Networks

Author : Vladimir Cherkassky
Publisher : Springer Science & Business Media
ISBN 13 : 3642791190
Total Pages : 414 pages
Book Rating : 4.6/5 (427 download)

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Book Synopsis From Statistics to Neural Networks by : Vladimir Cherkassky

Download or read book From Statistics to Neural Networks written by Vladimir Cherkassky and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 414 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Statistical Learning Using Neural Networks

Author : Basilio de Braganca Pereira
Publisher : CRC Press
ISBN 13 : 0429775547
Total Pages : 286 pages
Book Rating : 4.4/5 (297 download)

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Book Synopsis Statistical Learning Using Neural Networks by : Basilio de Braganca Pereira

Download or read book Statistical Learning Using Neural Networks written by Basilio de Braganca Pereira and published by CRC Press. This book was released on 2020-08-25 with total page 286 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Statistical Mechanics of Neural Networks

Author : Haiping Huang
Publisher : Springer Nature
ISBN 13 : 9811675708
Total Pages : 302 pages
Book Rating : 4.8/5 (116 download)

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Book Synopsis Statistical Mechanics of Neural Networks by : Haiping Huang

Download or read book Statistical Mechanics of Neural Networks written by Haiping Huang and published by Springer Nature. This book was released on 2022-01-04 with total page 302 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Statistical Field Theory for Neural Networks

Author : Moritz Helias
Publisher : Springer Nature
ISBN 13 : 303046444X
Total Pages : 203 pages
Book Rating : 4.0/5 (34 download)

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Book Synopsis Statistical Field Theory for Neural Networks by : Moritz Helias

Download or read book Statistical Field Theory for Neural Networks written by Moritz Helias and published by Springer Nature. This book was released on 2020-08-20 with total page 203 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Bayesian Nonparametrics via Neural Networks

Author : Herbert K. H. Lee
Publisher : SIAM
ISBN 13 : 9780898718423
Total Pages : 106 pages
Book Rating : 4.7/5 (184 download)

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Book Synopsis Bayesian Nonparametrics via Neural Networks by : Herbert K. H. Lee

Download or read book Bayesian Nonparametrics via Neural Networks written by Herbert K. H. Lee and published by SIAM. This book was released on 2004-01-01 with total page 106 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Neural Networks for Statistical Modeling

Author : Murray Smith
Publisher : Van Nostrand Reinhold Company
ISBN 13 :
Total Pages : 268 pages
Book Rating : 4.F/5 ( download)

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Book Synopsis Neural Networks for Statistical Modeling by : Murray Smith

Download or read book Neural Networks for Statistical Modeling written by Murray Smith and published by Van Nostrand Reinhold Company. This book was released on 1993 with total page 268 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Bayesian Learning for Neural Networks

Author : Radford M. Neal
Publisher : Springer Science & Business Media
ISBN 13 : 1461207452
Total Pages : 194 pages
Book Rating : 4.4/5 (612 download)

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Book Synopsis Bayesian Learning for Neural Networks by : Radford M. Neal

Download or read book Bayesian Learning for Neural Networks written by Radford M. Neal and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 194 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Pattern Recognition and Neural Networks

Author : Brian D. Ripley
Publisher : Cambridge University Press
ISBN 13 : 9780521717700
Total Pages : 420 pages
Book Rating : 4.7/5 (177 download)

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Book Synopsis Pattern Recognition and Neural Networks by : Brian D. Ripley

Download or read book Pattern Recognition and Neural Networks written by Brian D. Ripley and published by Cambridge University Press. This book was released on 2007 with total page 420 pages. Available in PDF, EPUB and Kindle. Book excerpt: This 1996 book explains the statistical framework for pattern recognition and machine learning, now in paperback.

Modern Analysis of Customer Surveys

Author : Ron S. Kenett
Publisher : John Wiley & Sons
ISBN 13 : 0470971282
Total Pages : 533 pages
Book Rating : 4.4/5 (79 download)

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Book Synopsis Modern Analysis of Customer Surveys by : Ron S. Kenett

Download or read book Modern Analysis of Customer Surveys written by Ron S. Kenett and published by John Wiley & Sons. This book was released on 2012-01-30 with total page 533 pages. Available in PDF, EPUB and Kindle. Book excerpt: Customer survey studies deals with customers, consumers and user satisfaction from a product or service. In practice, many of the customer surveys conducted by business and industry are analyzed in a very simple way, without using models or statistical methods. Typical reports include descriptive statistics and basic graphical displays. As demonstrated in this book, integrating such basic analysis with more advanced tools, provides insights on non-obvious patterns and important relationships between the survey variables. This knowledge can significantly affect the conclusions derived from a survey. Key features: Provides an integrated, case-studies based approach to analysing customer survey data. Presents a general introduction to customer surveys, within an organization’s business cycle. Contains classical techniques with modern and non standard tools. Focuses on probabilistic techniques from the area of statistics/data analysis and covers all major recent developments. Accompanied by a supporting website containing datasets and R scripts. Customer survey specialists, quality managers and market researchers will benefit from this book as well as specialists in marketing, data mining and business intelligence fields.

Neural Networks

Author : Gérard Dreyfus
Publisher : Springer Science & Business Media
ISBN 13 : 3540288473
Total Pages : 509 pages
Book Rating : 4.5/5 (42 download)

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Book Synopsis Neural Networks by : Gérard Dreyfus

Download or read book Neural Networks written by Gérard Dreyfus and published by Springer Science & Business Media. This book was released on 2005-11-25 with total page 509 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neural networks represent a powerful data processing technique that has reached maturity and broad application. When clearly understood and appropriately used, they are a mandatory component in the toolbox of any engineer who wants make the best use of the available data, in order to build models, make predictions, mine data, recognize shapes or signals, etc. Ranging from theoretical foundations to real-life applications, this book is intended to provide engineers and researchers with clear methodologies for taking advantage of neural networks in industrial, financial or banking applications, many instances of which are presented in the book. For the benefit of readers wishing to gain deeper knowledge of the topics, the book features appendices that provide theoretical details for greater insight, and algorithmic details for efficient programming and implementation. The chapters have been written by experts and edited to present a coherent and comprehensive, yet not redundant, practically oriented introduction.

Neural Networks for Pattern Recognition

Author : Christopher M. Bishop
Publisher : Oxford University Press
ISBN 13 : 0198538642
Total Pages : 501 pages
Book Rating : 4.1/5 (985 download)

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Book Synopsis Neural Networks for Pattern Recognition by : Christopher M. Bishop

Download or read book Neural Networks for Pattern Recognition written by Christopher M. Bishop and published by Oxford University Press. This book was released on 1995-11-23 with total page 501 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical pattern recognition; Probability density estimation; Single-layer networks; The multi-layer perceptron; Radial basis functions; Error functions; Parameter optimization algorithms; Pre-processing and feature extraction; Learning and generalization; Bayesian techniques; Appendix; References; Index.

Neural Networks

Author : Raul Rojas
Publisher : Springer Science & Business Media
ISBN 13 : 3642610684
Total Pages : 511 pages
Book Rating : 4.6/5 (426 download)

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Book Synopsis Neural Networks by : Raul Rojas

Download or read book Neural Networks written by Raul Rojas and published by Springer Science & Business Media. This book was released on 2013-06-29 with total page 511 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Neural Networks for Applied Sciences and Engineering

Author : Sandhya Samarasinghe
Publisher : CRC Press
ISBN 13 : 1420013068
Total Pages : 596 pages
Book Rating : 4.4/5 (2 download)

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Book Synopsis Neural Networks for Applied Sciences and Engineering by : Sandhya Samarasinghe

Download or read book Neural Networks for Applied Sciences and Engineering written by Sandhya Samarasinghe and published by CRC Press. This book was released on 2016-04-19 with total page 596 pages. Available in PDF, EPUB and Kindle. Book excerpt: In response to the exponentially increasing need to analyze vast amounts of data, Neural Networks for Applied Sciences and Engineering: From Fundamentals to Complex Pattern Recognition provides scientists with a simple but systematic introduction to neural networks. Beginning with an introductory discussion on the role of neural networks in

Data Analysis and Information Systems

Author : Gesellschaft für Klassifikation. Jahrestagung
Publisher : Springer
ISBN 13 :
Total Pages : 572 pages
Book Rating : 4.3/5 (91 download)

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Book Synopsis Data Analysis and Information Systems by : Gesellschaft für Klassifikation. Jahrestagung

Download or read book Data Analysis and Information Systems written by Gesellschaft für Klassifikation. Jahrestagung and published by Springer. This book was released on 1996-03-18 with total page 572 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume presents methods for the analysis of quantitative and qualitative data, and for the processing and ordering of symbolic or linguistic information. The 45 papers combine methods from exploratory and inferential statistics with mathematical and numerical approaches, investigate conceptual classification and ordering structures and describe recent developments for knowledge-based information systems. The contributions are grouped into seven chapters: 1. Classification and Clustering, 2. Uncertainty and Fuzziness, 3. Methods of Data Analysis and Applications, 4. Statistical Models and Methods, 5. Bayesian Learning, 6. Conceptual Classification, Knowledge Ordering and Information Systems, 7. Linguistics and Dialectometry.

Feedforward Neural Network Methodology

Author : Terrence L. Fine
Publisher : Springer Science & Business Media
ISBN 13 : 0387226494
Total Pages : 353 pages
Book Rating : 4.3/5 (872 download)

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Book Synopsis Feedforward Neural Network Methodology by : Terrence L. Fine

Download or read book Feedforward Neural Network Methodology written by Terrence L. Fine and published by Springer Science & Business Media. This book was released on 2006-04-06 with total page 353 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Deep Neural Networks in a Mathematical Framework

Author : Anthony L. Caterini
Publisher : Springer
ISBN 13 : 3319753045
Total Pages : 95 pages
Book Rating : 4.3/5 (197 download)

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Book Synopsis Deep Neural Networks in a Mathematical Framework by : Anthony L. Caterini

Download or read book Deep Neural Networks in a Mathematical Framework written by Anthony L. Caterini and published by Springer. This book was released on 2018-03-22 with total page 95 pages. Available in PDF, EPUB and Kindle. Book excerpt: This SpringerBrief describes how to build a rigorous end-to-end mathematical framework for deep neural networks. The authors provide tools to represent and describe neural networks, casting previous results in the field in a more natural light. In particular, the authors derive gradient descent algorithms in a unified way for several neural network structures, including multilayer perceptrons, convolutional neural networks, deep autoencoders and recurrent neural networks. Furthermore, the authors developed framework is both more concise and mathematically intuitive than previous representations of neural networks. This SpringerBrief is one step towards unlocking the black box of Deep Learning. The authors believe that this framework will help catalyze further discoveries regarding the mathematical properties of neural networks.This SpringerBrief is accessible not only to researchers, professionals and students working and studying in the field of deep learning, but also to those outside of the neutral network community.