Nonlinear Optimization Approaches For Training Neural Networks

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Nonlinear Optimization Approaches for Training Neural Networks

Author : George Magoulas
Publisher : Springer
ISBN 13 : 9781441916617
Total Pages : 500 pages
Book Rating : 4.9/5 (166 download)

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Book Synopsis Nonlinear Optimization Approaches for Training Neural Networks by : George Magoulas

Download or read book Nonlinear Optimization Approaches for Training Neural Networks written by George Magoulas and published by Springer. This book was released on 2015-12-24 with total page 500 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book examines how nonlinear optimization techniques can be applied to training and testing neural networks. It includes both well-known and recently-developed network training methods including deterministic nonlinear optimization methods, stochastic nonlinear optimization methods, and advanced training schemes which combine both deterministic and stochastic components. The convergence analysis and convergence proofs of these techniques are presented as well as real applications of neural networks in areas such as pattern classification, bioinformatics, biomedicine, and finance. Nonlinear optimization methods are applied extensively in the design of training protocols for artificial neural networks used in industry and academia. Such techniques allow for the implementation of dynamic unsupervised neural network training without requiring the fine tuning of several heuristic parameters. "Nonlinear Optimization Approaches for Training Neural Networks" is a response to the growing demand for innovations in this area of research. This monograph presents a wide range of approaches to neural networks training providing theoretical justification for network behavior based on the theory of nonlinear optimization. It presents training algorithms, and theoretical results on their convergence and implementations through pseudocode. This approach offers the reader an explanation of the performance of the various methods, and a better understanding of the individual characteristics of the various methods, their differences/advantages and interrelationships. This improved perspective allows the reader to choose the best network training method without spending too much effort configuring highly sensitive heuristic parameters. This book can serve as an excellent guide for researchers, graduate students, and lecturers interested in the development of neural networks and their training.

Neural Networks in Optimization

Author : Xiang-Sun Zhang
Publisher : Springer Science & Business Media
ISBN 13 : 9780792365150
Total Pages : 394 pages
Book Rating : 4.3/5 (651 download)

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Book Synopsis Neural Networks in Optimization by : Xiang-Sun Zhang

Download or read book Neural Networks in Optimization written by Xiang-Sun Zhang and published by Springer Science & Business Media. This book was released on 2000-10-31 with total page 394 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book consists of three parts. The first part introduces concepts and algorithms in optimization theory, which have been used in neural network research. The second part covers main neural network models and their theoretical analysis. The third part of the book introduces various neural network models for solving nonlinear programming problems and combinatorial optimization problems. Audience: Graduate students and researchers who are interested in the intersection of optimization theory and artificial neural networks. The book is appropriate for graduate courses.

Nonlinear System Identification

Author : Oliver Nelles
Publisher : Springer Science & Business Media
ISBN 13 : 3662043238
Total Pages : 786 pages
Book Rating : 4.6/5 (62 download)

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Book Synopsis Nonlinear System Identification by : Oliver Nelles

Download or read book Nonlinear System Identification written by Oliver Nelles and published by Springer Science & Business Media. This book was released on 2013-03-09 with total page 786 pages. Available in PDF, EPUB and Kindle. Book excerpt: Written from an engineering point of view, this book covers the most common and important approaches for the identification of nonlinear static and dynamic systems. The book also provides the reader with the necessary background on optimization techniques, making it fully self-contained. The new edition includes exercises.

Optimization for Machine Learning

Author : Suvrit Sra
Publisher : MIT Press
ISBN 13 : 026201646X
Total Pages : 509 pages
Book Rating : 4.2/5 (62 download)

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Book Synopsis Optimization for Machine Learning by : Suvrit Sra

Download or read book Optimization for Machine Learning written by Suvrit Sra and published by MIT Press. This book was released on 2012 with total page 509 pages. Available in PDF, EPUB and Kindle. Book excerpt: An up-to-date account of the interplay between optimization and machine learning, accessible to students and researchers in both communities. The interplay between optimization and machine learning is one of the most important developments in modern computational science. Optimization formulations and methods are proving to be vital in designing algorithms to extract essential knowledge from huge volumes of data. Machine learning, however, is not simply a consumer of optimization technology but a rapidly evolving field that is itself generating new optimization ideas. This book captures the state of the art of the interaction between optimization and machine learning in a way that is accessible to researchers in both fields. Optimization approaches have enjoyed prominence in machine learning because of their wide applicability and attractive theoretical properties. The increasing complexity, size, and variety of today's machine learning models call for the reassessment of existing assumptions. This book starts the process of reassessment. It describes the resurgence in novel contexts of established frameworks such as first-order methods, stochastic approximations, convex relaxations, interior-point methods, and proximal methods. It also devotes attention to newer themes such as regularized optimization, robust optimization, gradient and subgradient methods, splitting techniques, and second-order methods. Many of these techniques draw inspiration from other fields, including operations research, theoretical computer science, and subfields of optimization. The book will enrich the ongoing cross-fertilization between the machine learning community and these other fields, and within the broader optimization community.

Nonlinear Programming

Author : Dimitri P. Bertsekas
Publisher : Goodman Publishers
ISBN 13 :
Total Pages : 806 pages
Book Rating : 4.3/5 (97 download)

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Book Synopsis Nonlinear Programming by : Dimitri P. Bertsekas

Download or read book Nonlinear Programming written by Dimitri P. Bertsekas and published by Goodman Publishers. This book was released on 1999 with total page 806 pages. Available in PDF, EPUB and Kindle. Book excerpt:

An Introduction to Optimization

Author : Edwin K. P. Chong
Publisher : John Wiley & Sons
ISBN 13 : 1119877652
Total Pages : 677 pages
Book Rating : 4.1/5 (198 download)

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Book Synopsis An Introduction to Optimization by : Edwin K. P. Chong

Download or read book An Introduction to Optimization written by Edwin K. P. Chong and published by John Wiley & Sons. This book was released on 2023-09-11 with total page 677 pages. Available in PDF, EPUB and Kindle. Book excerpt: An Introduction to Optimization Accessible introductory textbook on optimization theory and methods, with an emphasis on engineering design, featuring MATLAB® exercises and worked examples Fully updated to reflect modern developments in the field, the Fifth Edition of An Introduction to Optimization fills the need for an accessible, yet rigorous, introduction to optimization theory and methods, featuring innovative coverage and a straightforward approach. The book begins with a review of basic definitions and notations while also providing the related fundamental background of linear algebra, geometry, and calculus. With this foundation, the authors explore the essential topics of unconstrained optimization problems, linear programming problems, and nonlinear constrained optimization. In addition, the book includes an introduction to artificial neural networks, convex optimization, multi-objective optimization, and applications of optimization in machine learning. Numerous diagrams and figures found throughout the book complement the written presentation of key concepts, and each chapter is followed by MATLAB® exercises and practice problems that reinforce the discussed theory and algorithms. The Fifth Edition features a new chapter on Lagrangian (nonlinear) duality, expanded coverage on matrix games, projected gradient algorithms, machine learning, and numerous new exercises at the end of each chapter. An Introduction to Optimization includes information on: The mathematical definitions, notations, and relations from linear algebra, geometry, and calculus used in optimization Optimization algorithms, covering one-dimensional search, randomized search, and gradient, Newton, conjugate direction, and quasi-Newton methods Linear programming methods, covering the simplex algorithm, interior point methods, and duality Nonlinear constrained optimization, covering theory and algorithms, convex optimization, and Lagrangian duality Applications of optimization in machine learning, including neural network training, classification, stochastic gradient descent, linear regression, logistic regression, support vector machines, and clustering. An Introduction to Optimization is an ideal textbook for a one- or two-semester senior undergraduate or beginning graduate course in optimization theory and methods. The text is also of value for researchers and professionals in mathematics, operations research, electrical engineering, economics, statistics, and business.

Advances in Neural Networks Research

Author : D.C. Wunsch II
Publisher : Elsevier
ISBN 13 : 9780080443201
Total Pages : 438 pages
Book Rating : 4.4/5 (432 download)

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Book Synopsis Advances in Neural Networks Research by : D.C. Wunsch II

Download or read book Advances in Neural Networks Research written by D.C. Wunsch II and published by Elsevier. This book was released on 2003-08-22 with total page 438 pages. Available in PDF, EPUB and Kindle. Book excerpt: IJCNN is the flagship conference of the INNS, as well as the IEEE Neural Networks Society. It has arguably been the preeminent conference in the field, even as neural network conferences have proliferated and specialized. As the number of conferences has grown, its strongest competition has migrated away from an emphasis on neural networks. IJCNN has embraced the proliferation of spin-off and related fields (see the topic list, below), while maintaining a core emphasis befitting its name. It has also succeeded in enforcing an emphasis on quality.

Second-Order Methods for Neural Networks

Author : Adrian J. Shepherd
Publisher : Springer Science & Business Media
ISBN 13 : 1447109538
Total Pages : 156 pages
Book Rating : 4.4/5 (471 download)

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Book Synopsis Second-Order Methods for Neural Networks by : Adrian J. Shepherd

Download or read book Second-Order Methods for Neural Networks written by Adrian J. Shepherd and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 156 pages. Available in PDF, EPUB and Kindle. Book excerpt: About This Book This book is about training methods - in particular, fast second-order training methods - for multi-layer perceptrons (MLPs). MLPs (also known as feed-forward neural networks) are the most widely-used class of neural network. Over the past decade MLPs have achieved increasing popularity among scientists, engineers and other professionals as tools for tackling a wide variety of information processing tasks. In common with all neural networks, MLPsare trained (rather than programmed) to carryout the chosen information processing function. Unfortunately, the (traditional' method for trainingMLPs- the well-knownbackpropagation method - is notoriously slow and unreliable when applied to many prac tical tasks. The development of fast and reliable training algorithms for MLPsis one of the most important areas ofresearch within the entire field of neural computing. The main purpose of this book is to bring to a wider audience a range of alternative methods for training MLPs, methods which have proved orders of magnitude faster than backpropagation when applied to many training tasks. The book also addresses the well-known (local minima' problem, and explains ways in which fast training methods can be com bined with strategies for avoiding (or escaping from) local minima. All the methods described in this book have a strong theoretical foundation, drawing on such diverse mathematical fields as classical optimisation theory, homotopic theory and stochastic approximation theory.

An Introduction to Neural Network Methods for Differential Equations

Author : Neha Yadav
Publisher : Springer
ISBN 13 : 9401798168
Total Pages : 124 pages
Book Rating : 4.4/5 (17 download)

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Book Synopsis An Introduction to Neural Network Methods for Differential Equations by : Neha Yadav

Download or read book An Introduction to Neural Network Methods for Differential Equations written by Neha Yadav and published by Springer. This book was released on 2015-02-26 with total page 124 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces a variety of neural network methods for solving differential equations arising in science and engineering. The emphasis is placed on a deep understanding of the neural network techniques, which has been presented in a mostly heuristic and intuitive manner. This approach will enable the reader to understand the working, efficiency and shortcomings of each neural network technique for solving differential equations. The objective of this book is to provide the reader with a sound understanding of the foundations of neural networks and a comprehensive introduction to neural network methods for solving differential equations together with recent developments in the techniques and their applications. The book comprises four major sections. Section I consists of a brief overview of differential equations and the relevant physical problems arising in science and engineering. Section II illustrates the history of neural networks starting from their beginnings in the 1940s through to the renewed interest of the 1980s. A general introduction to neural networks and learning technologies is presented in Section III. This section also includes the description of the multilayer perceptron and its learning methods. In Section IV, the different neural network methods for solving differential equations are introduced, including discussion of the most recent developments in the field. Advanced students and researchers in mathematics, computer science and various disciplines in science and engineering will find this book a valuable reference source.

Neural Networks in Business Forecasting

Author : G. Peter Zhang
Publisher : IGI Global
ISBN 13 : 1591401771
Total Pages : 296 pages
Book Rating : 4.5/5 (914 download)

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Book Synopsis Neural Networks in Business Forecasting by : G. Peter Zhang

Download or read book Neural Networks in Business Forecasting written by G. Peter Zhang and published by IGI Global. This book was released on 2004-01-01 with total page 296 pages. Available in PDF, EPUB and Kindle. Book excerpt: Forecasting is one of the most important activities that form the basis for strategic, tactical, and operational decisions in all business organizations. Recently, neural networks have emerged as an important tool for business forecasting. Neural Networks in Business Forecasting provides researchers and practitioners with some recent advances in applying neural networks to business forecasting. A number of case studies demonstrating the innovative or successful applications of neural networks to many areas of business as well as methods to improve neural network forecasting performance are presented.

Recurrent Neural Networks

Author : Larry Medsker
Publisher : CRC Press
ISBN 13 : 9781420049176
Total Pages : 414 pages
Book Rating : 4.0/5 (491 download)

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Book Synopsis Recurrent Neural Networks by : Larry Medsker

Download or read book Recurrent Neural Networks written by Larry Medsker and published by CRC Press. This book was released on 1999-12-20 with total page 414 pages. Available in PDF, EPUB and Kindle. Book excerpt: With existent uses ranging from motion detection to music synthesis to financial forecasting, recurrent neural networks have generated widespread attention. The tremendous interest in these networks drives Recurrent Neural Networks: Design and Applications, a summary of the design, applications, current research, and challenges of this subfield of artificial neural networks. This overview incorporates every aspect of recurrent neural networks. It outlines the wide variety of complex learning techniques and associated research projects. Each chapter addresses architectures, from fully connected to partially connected, including recurrent multilayer feedforward. It presents problems involving trajectories, control systems, and robotics, as well as RNN use in chaotic systems. The authors also share their expert knowledge of ideas for alternate designs and advances in theoretical aspects. The dynamical behavior of recurrent neural networks is useful for solving problems in science, engineering, and business. This approach will yield huge advances in the coming years. Recurrent Neural Networks illuminates the opportunities and provides you with a broad view of the current events in this rich field.

Neural Nets Wirn Vietri '95 - Proceedings Of The Vii Italian Workshop

Author : Maria Marinaro
Publisher : World Scientific
ISBN 13 : 9814548006
Total Pages : 334 pages
Book Rating : 4.8/5 (145 download)

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Book Synopsis Neural Nets Wirn Vietri '95 - Proceedings Of The Vii Italian Workshop by : Maria Marinaro

Download or read book Neural Nets Wirn Vietri '95 - Proceedings Of The Vii Italian Workshop written by Maria Marinaro and published by World Scientific. This book was released on 1996-01-29 with total page 334 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume contains the proceedings of the seventh Italian Workshop on Neural Nets WIRN VIETRI '95, organized by the International Institute for Advanced Scientific Studies 'E R Caianiello' (IIASS) and Società Italiana Reti Neuroniche (SIREN).The spectrum of contributors and participants covers the activity of Italian research in the field. The papers of the two invited speakers, M J Jordan ('Sigmoid Belief Networks') and E Oja ('Principal and Independent Component Analysis'), and the two reviews ('Fast Learning Algorithms for Feedforward NN' and 'ANN Ensembles: a Bayesian Standpoint') complete the highly qualified contents of the volume.

Neural Networks and Statistical Learning

Author : Ke-Lin Du
Publisher : Springer Nature
ISBN 13 : 1447174526
Total Pages : 988 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 Nature. This book was released on 2019-09-12 with total page 988 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Encyclopedia of Optimization

Author : Christodoulos A. Floudas
Publisher : Springer Science & Business Media
ISBN 13 : 0387747583
Total Pages : 4646 pages
Book Rating : 4.3/5 (877 download)

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Book Synopsis Encyclopedia of Optimization by : Christodoulos A. Floudas

Download or read book Encyclopedia of Optimization written by Christodoulos A. Floudas and published by Springer Science & Business Media. This book was released on 2008-09-04 with total page 4646 pages. Available in PDF, EPUB and Kindle. Book excerpt: The goal of the Encyclopedia of Optimization is to introduce the reader to a complete set of topics that show the spectrum of research, the richness of ideas, and the breadth of applications that has come from this field. The second edition builds on the success of the former edition with more than 150 completely new entries, designed to ensure that the reference addresses recent areas where optimization theories and techniques have advanced. Particularly heavy attention resulted in health science and transportation, with entries such as "Algorithms for Genomics", "Optimization and Radiotherapy Treatment Design", and "Crew Scheduling".

Network Optimization

Author : Panos M. Pardalos
Publisher : Springer Science & Business Media
ISBN 13 : 3642591795
Total Pages : 495 pages
Book Rating : 4.6/5 (425 download)

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Book Synopsis Network Optimization by : Panos M. Pardalos

Download or read book Network Optimization written by Panos M. Pardalos and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 495 pages. Available in PDF, EPUB and Kindle. Book excerpt: Network optimization is important in the modeling of problems and processes from such fields as engineering, computer science, operations research, transportation, telecommunication, decision support systems, manufacturing, and airline scheduling. Recent advances in data structures, computer technology, and algorithm development have made it possible to solve classes of network optimization problems that until recently were intractable. The refereed papers in this volume reflect the interdisciplinary efforts of a large group of scientists from academia and industry to model and solve complicated large-scale network optimization problems.

Parallel Computing in Optimization

Author : A. Migdalas
Publisher : Springer Science & Business Media
ISBN 13 : 1461334004
Total Pages : 596 pages
Book Rating : 4.4/5 (613 download)

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Book Synopsis Parallel Computing in Optimization by : A. Migdalas

Download or read book Parallel Computing in Optimization written by A. Migdalas and published by Springer Science & Business Media. This book was released on 2013-12-01 with total page 596 pages. Available in PDF, EPUB and Kindle. Book excerpt: During the last three decades, breakthroughs in computer technology have made a tremendous impact on optimization. In particular, parallel computing has made it possible to solve larger and computationally more difficult prob lems. This volume contains mainly lecture notes from a Nordic Summer School held at the Linkoping Institute of Technology, Sweden in August 1995. In order to make the book more complete, a few authors were invited to contribute chapters that were not part of the course on this first occasion. The purpose of this Nordic course in advanced studies was three-fold. One goal was to introduce the students to the new achievements in a new and very active field, bring them close to world leading researchers, and strengthen their competence in an area with internationally explosive rate of growth. A second goal was to strengthen the bonds between students from different Nordic countries, and to encourage collaboration and joint research ventures over the borders. In this respect, the course built further on the achievements of the "Nordic Network in Mathematical Programming" , which has been running during the last three years with the support ofthe Nordic Council for Advanced Studies (NorFA). The final goal was to produce literature on the particular subject, which would be available to both the participating students and to the students of the "next generation" .

Neural Networks Theory

Author : Alexander I. Galushkin
Publisher : Springer Science & Business Media
ISBN 13 : 3540481257
Total Pages : 396 pages
Book Rating : 4.5/5 (44 download)

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Book Synopsis Neural Networks Theory by : Alexander I. Galushkin

Download or read book Neural Networks Theory written by Alexander I. Galushkin and published by Springer Science & Business Media. This book was released on 2007-10-29 with total page 396 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book, written by a leader in neural network theory in Russia, uses mathematical methods in combination with complexity theory, nonlinear dynamics and optimization. It details more than 40 years of Soviet and Russian neural network research and presents a systematized methodology of neural networks synthesis. The theory is expansive: covering not just traditional topics such as network architecture but also neural continua in function spaces as well.