Ridge Functions and Applications in Neural Networks

Ridge Functions and Applications in Neural Networks

Author: Vugar E. Ismailov

Publisher: American Mathematical Society

Published: 2021-12-17

Total Pages: 186

ISBN-13: 1470467658

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Recent years have witnessed a growth of interest in the special functions called ridge functions. These functions appear in various fields and under various guises. They appear in partial differential equations (where they are called plane waves), in computerized tomography, and in statistics. Ridge functions are also the underpinnings of many central models in neural network theory. In this book various approximation theoretic properties of ridge functions are described. This book also describes properties of generalized ridge functions, and their relation to linear superpositions and Kolmogorov's famous superposition theorem. In the final part of the book, a single and two hidden layer neural networks are discussed. The results obtained in this part are based on properties of ordinary and generalized ridge functions. Novel aspects of the universal approximation property of feedforward neural networks are revealed. This book will be of interest to advanced graduate students and researchers working in functional analysis, approximation theory, and the theory of real functions, and will be of particular interest to those wishing to learn more about neural network theory and applications and other areas where ridge functions are used.


Book Synopsis Ridge Functions and Applications in Neural Networks by : Vugar E. Ismailov

Download or read book Ridge Functions and Applications in Neural Networks written by Vugar E. Ismailov and published by American Mathematical Society. This book was released on 2021-12-17 with total page 186 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recent years have witnessed a growth of interest in the special functions called ridge functions. These functions appear in various fields and under various guises. They appear in partial differential equations (where they are called plane waves), in computerized tomography, and in statistics. Ridge functions are also the underpinnings of many central models in neural network theory. In this book various approximation theoretic properties of ridge functions are described. This book also describes properties of generalized ridge functions, and their relation to linear superpositions and Kolmogorov's famous superposition theorem. In the final part of the book, a single and two hidden layer neural networks are discussed. The results obtained in this part are based on properties of ordinary and generalized ridge functions. Novel aspects of the universal approximation property of feedforward neural networks are revealed. This book will be of interest to advanced graduate students and researchers working in functional analysis, approximation theory, and the theory of real functions, and will be of particular interest to those wishing to learn more about neural network theory and applications and other areas where ridge functions are used.

Ridge Functions

Ridge Functions

Author: Allan Pinkus

Publisher: Cambridge University Press

Published: 2015-08-07

Total Pages: 218

ISBN-13: 1107124395

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Presents the state of the art in the theory of ridge functions, providing a solid theoretical foundation.


Book Synopsis Ridge Functions by : Allan Pinkus

Download or read book Ridge Functions written by Allan Pinkus and published by Cambridge University Press. This book was released on 2015-08-07 with total page 218 pages. Available in PDF, EPUB and Kindle. Book excerpt: Presents the state of the art in the theory of ridge functions, providing a solid theoretical foundation.

Ridge Functions

Ridge Functions

Author: Allan Pinkus

Publisher:

Published: 2016

Total Pages: 207

ISBN-13: 9781316439685

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Ridge functions are a rich class of simple multivariate functions which have found applications in a variety of areas. These include partial differential equations (where they are sometimes termed 'plane waves'), computerised tomography, projection pursuit in the analysis of large multivariate data sets, the MLP model in neural networks, Waring's problem over linear forms, and approximation theory. Ridge Functions is the first book devoted to studying them as entities in and of themselves. The author describes their central properties and provides a solid theoretical foundation for researchers working in areas such as approximation or data science. He also includes an extensive bibliography and discusses some of the unresolved questions that may set the course for future research in the field.


Book Synopsis Ridge Functions by : Allan Pinkus

Download or read book Ridge Functions written by Allan Pinkus and published by . This book was released on 2016 with total page 207 pages. Available in PDF, EPUB and Kindle. Book excerpt: Ridge functions are a rich class of simple multivariate functions which have found applications in a variety of areas. These include partial differential equations (where they are sometimes termed 'plane waves'), computerised tomography, projection pursuit in the analysis of large multivariate data sets, the MLP model in neural networks, Waring's problem over linear forms, and approximation theory. Ridge Functions is the first book devoted to studying them as entities in and of themselves. The author describes their central properties and provides a solid theoretical foundation for researchers working in areas such as approximation or data science. He also includes an extensive bibliography and discusses some of the unresolved questions that may set the course for future research in the field.

Analysis and Applications of Artificial Neural Networks

Analysis and Applications of Artificial Neural Networks

Author: Leo P. J. Veelenturf

Publisher:

Published: 1995

Total Pages: 284

ISBN-13:

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This volume is an analysis of the behaviour of the three types of neural networks: the binary perceptron, the continuous perceptron and the self-organizing neural network. Analysis is largely mathematical but concepts are also explained through practical examples.


Book Synopsis Analysis and Applications of Artificial Neural Networks by : Leo P. J. Veelenturf

Download or read book Analysis and Applications of Artificial Neural Networks written by Leo P. J. Veelenturf and published by . This book was released on 1995 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume is an analysis of the behaviour of the three types of neural networks: the binary perceptron, the continuous perceptron and the self-organizing neural network. Analysis is largely mathematical but concepts are also explained through practical examples.

Neural Network Applications in Control

Neural Network Applications in Control

Author: George William Irwin

Publisher: IET

Published: 1995

Total Pages: 320

ISBN-13: 9780852968529

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The aim is to present an introduction to, and an overview of, the present state of neural network research and development, with an emphasis on control systems application studies. The book is useful to a range of levels of reader. The earlier chapters introduce the more popular networks and the fundamental control principles, these are followed by a series of application studies, most of which are industrially based, and the book concludes with a consideration of some recent research.


Book Synopsis Neural Network Applications in Control by : George William Irwin

Download or read book Neural Network Applications in Control written by George William Irwin and published by IET. This book was released on 1995 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: The aim is to present an introduction to, and an overview of, the present state of neural network research and development, with an emphasis on control systems application studies. The book is useful to a range of levels of reader. The earlier chapters introduce the more popular networks and the fundamental control principles, these are followed by a series of application studies, most of which are industrially based, and the book concludes with a consideration of some recent research.

Neural Networks and Computing

Neural Networks and Computing

Author:

Publisher:

Published:

Total Pages:

ISBN-13: 1908979372

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Book Synopsis Neural Networks and Computing by :

Download or read book Neural Networks and Computing written by and published by . This book was released on with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Process Neural Networks

Process Neural Networks

Author: Xingui He

Publisher: Springer Science & Business Media

Published: 2010-07-05

Total Pages: 240

ISBN-13: 3540737626

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For the first time, this book sets forth the concept and model for a process neural network. You’ll discover how a process neural network expands the mapping relationship between the input and output of traditional neural networks and greatly enhances the expression capability of artificial neural networks. Detailed illustrations help you visualize information processing flow and the mapping relationship between inputs and outputs.


Book Synopsis Process Neural Networks by : Xingui He

Download or read book Process Neural Networks written by Xingui He and published by Springer Science & Business Media. This book was released on 2010-07-05 with total page 240 pages. Available in PDF, EPUB and Kindle. Book excerpt: For the first time, this book sets forth the concept and model for a process neural network. You’ll discover how a process neural network expands the mapping relationship between the input and output of traditional neural networks and greatly enhances the expression capability of artificial neural networks. Detailed illustrations help you visualize information processing flow and the mapping relationship between inputs and outputs.

Machine Learning for the Physical Sciences

Machine Learning for the Physical Sciences

Author: Carlo Requião da Cunha

Publisher: CRC Press

Published: 2023-12-05

Total Pages: 289

ISBN-13: 1003821146

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Machine learning is an exciting topic with a myriad of applications. However, most textbooks are targeted towards computer science students. This, however, creates a complication for scientists across the physical sciences that also want to understand the main concepts of machine learning and look ahead to applica- tions and advancements in their fields. This textbook bridges this gap, providing an introduction to the mathematical foundations for the main algorithms used in machine learning for those from the physical sciences, without a formal background in computer science. It demon- strates how machine learning can be used to solve problems in physics and engineering, targeting senior undergraduate and graduate students in physics and electrical engineering, alongside advanced researchers. Key Features: Includes detailed algorithms Supplemented by codes in Julia: a high-performing language and one that is easy to read for those in the natural sciences All algorithms are presented with a good mathematical background


Book Synopsis Machine Learning for the Physical Sciences by : Carlo Requião da Cunha

Download or read book Machine Learning for the Physical Sciences written by Carlo Requião da Cunha and published by CRC Press. This book was released on 2023-12-05 with total page 289 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning is an exciting topic with a myriad of applications. However, most textbooks are targeted towards computer science students. This, however, creates a complication for scientists across the physical sciences that also want to understand the main concepts of machine learning and look ahead to applica- tions and advancements in their fields. This textbook bridges this gap, providing an introduction to the mathematical foundations for the main algorithms used in machine learning for those from the physical sciences, without a formal background in computer science. It demon- strates how machine learning can be used to solve problems in physics and engineering, targeting senior undergraduate and graduate students in physics and electrical engineering, alongside advanced researchers. Key Features: Includes detailed algorithms Supplemented by codes in Julia: a high-performing language and one that is easy to read for those in the natural sciences All algorithms are presented with a good mathematical background

Recent Advances in Harmonic Analysis and Applications

Recent Advances in Harmonic Analysis and Applications

Author: Dmitriy Bilyk

Publisher: Springer Science & Business Media

Published: 2012-10-16

Total Pages: 400

ISBN-13: 1461445647

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Recent Advances in Harmonic Analysis and Applications features selected contributions from the AMS conference which took place at Georgia Southern University, Statesboro in 2011 in honor of Professor Konstantin Oskolkov's 65th birthday. The contributions are based on two special sessions, namely "Harmonic Analysis and Applications" and "Sparse Data Representations and Applications." Topics covered range from Banach space geometry to classical harmonic analysis and partial differential equations. Survey and expository articles by leading experts in their corresponding fields are included, and the volume also features selected high quality papers exploring new results and trends in Muckenhoupt-Sawyer theory, orthogonal polynomials, trigonometric series, approximation theory, Bellman functions and applications in differential equations. Graduate students and researchers in analysis will be particularly interested in the articles which emphasize remarkable connections between analysis and analytic number theory. The readers will learn about recent mathematical developments and directions for future work in the unexpected and surprising interaction between abstract problems in additive number theory and experimentally discovered optical phenomena in physics. This book will be useful for number theorists, harmonic analysts, algorithmists in multi-dimensional signal processing and experts in physics and partial differential equations.


Book Synopsis Recent Advances in Harmonic Analysis and Applications by : Dmitriy Bilyk

Download or read book Recent Advances in Harmonic Analysis and Applications written by Dmitriy Bilyk and published by Springer Science & Business Media. This book was released on 2012-10-16 with total page 400 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recent Advances in Harmonic Analysis and Applications features selected contributions from the AMS conference which took place at Georgia Southern University, Statesboro in 2011 in honor of Professor Konstantin Oskolkov's 65th birthday. The contributions are based on two special sessions, namely "Harmonic Analysis and Applications" and "Sparse Data Representations and Applications." Topics covered range from Banach space geometry to classical harmonic analysis and partial differential equations. Survey and expository articles by leading experts in their corresponding fields are included, and the volume also features selected high quality papers exploring new results and trends in Muckenhoupt-Sawyer theory, orthogonal polynomials, trigonometric series, approximation theory, Bellman functions and applications in differential equations. Graduate students and researchers in analysis will be particularly interested in the articles which emphasize remarkable connections between analysis and analytic number theory. The readers will learn about recent mathematical developments and directions for future work in the unexpected and surprising interaction between abstract problems in additive number theory and experimentally discovered optical phenomena in physics. This book will be useful for number theorists, harmonic analysts, algorithmists in multi-dimensional signal processing and experts in physics and partial differential equations.

Theory of Ridge Regression Estimation with Applications

Theory of Ridge Regression Estimation with Applications

Author: A. K. Md. Ehsanes Saleh

Publisher: John Wiley & Sons

Published: 2019-02-12

Total Pages: 384

ISBN-13: 1118644611

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A guide to the systematic analytical results for ridge, LASSO, preliminary test, and Stein-type estimators with applications Theory of Ridge Regression Estimation with Applications offers a comprehensive guide to the theory and methods of estimation. Ridge regression and LASSO are at the center of all penalty estimators in a range of standard models that are used in many applied statistical analyses. Written by noted experts in the field, the book contains a thorough introduction to penalty and shrinkage estimation and explores the role that ridge, LASSO, and logistic regression play in the computer intensive area of neural network and big data analysis. Designed to be accessible, the book presents detailed coverage of the basic terminology related to various models such as the location and simple linear models, normal and rank theory-based ridge, LASSO, preliminary test and Stein-type estimators. The authors also include problem sets to enhance learning. This book is a volume in the Wiley Series in Probability and Statistics series that provides essential and invaluable reading for all statisticians. This important resource: Offers theoretical coverage and computer-intensive applications of the procedures presented Contains solutions and alternate methods for prediction accuracy and selecting model procedures Presents the first book to focus on ridge regression and unifies past research with current methodology Uses R throughout the text and includes a companion website containing convenient data sets Written for graduate students, practitioners, and researchers in various fields of science, Theory of Ridge Regression Estimation with Applications is an authoritative guide to the theory and methodology of statistical estimation.


Book Synopsis Theory of Ridge Regression Estimation with Applications by : A. K. Md. Ehsanes Saleh

Download or read book Theory of Ridge Regression Estimation with Applications written by A. K. Md. Ehsanes Saleh and published by John Wiley & Sons. This book was released on 2019-02-12 with total page 384 pages. Available in PDF, EPUB and Kindle. Book excerpt: A guide to the systematic analytical results for ridge, LASSO, preliminary test, and Stein-type estimators with applications Theory of Ridge Regression Estimation with Applications offers a comprehensive guide to the theory and methods of estimation. Ridge regression and LASSO are at the center of all penalty estimators in a range of standard models that are used in many applied statistical analyses. Written by noted experts in the field, the book contains a thorough introduction to penalty and shrinkage estimation and explores the role that ridge, LASSO, and logistic regression play in the computer intensive area of neural network and big data analysis. Designed to be accessible, the book presents detailed coverage of the basic terminology related to various models such as the location and simple linear models, normal and rank theory-based ridge, LASSO, preliminary test and Stein-type estimators. The authors also include problem sets to enhance learning. This book is a volume in the Wiley Series in Probability and Statistics series that provides essential and invaluable reading for all statisticians. This important resource: Offers theoretical coverage and computer-intensive applications of the procedures presented Contains solutions and alternate methods for prediction accuracy and selecting model procedures Presents the first book to focus on ridge regression and unifies past research with current methodology Uses R throughout the text and includes a companion website containing convenient data sets Written for graduate students, practitioners, and researchers in various fields of science, Theory of Ridge Regression Estimation with Applications is an authoritative guide to the theory and methodology of statistical estimation.