Support Vector Machines And Perceptrons

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Support Vector Machines and Perceptrons

Author : M.N. Murty
Publisher : Springer
ISBN 13 : 3319410636
Total Pages : 103 pages
Book Rating : 4.3/5 (194 download)

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Book Synopsis Support Vector Machines and Perceptrons by : M.N. Murty

Download or read book Support Vector Machines and Perceptrons written by M.N. Murty and published by Springer. This book was released on 2016-08-16 with total page 103 pages. Available in PDF, EPUB and Kindle. Book excerpt: This work reviews the state of the art in SVM and perceptron classifiers. A Support Vector Machine (SVM) is easily the most popular tool for dealing with a variety of machine-learning tasks, including classification. SVMs are associated with maximizing the margin between two classes. The concerned optimization problem is a convex optimization guaranteeing a globally optimal solution. The weight vector associated with SVM is obtained by a linear combination of some of the boundary and noisy vectors. Further, when the data are not linearly separable, tuning the coefficient of the regularization term becomes crucial. Even though SVMs have popularized the kernel trick, in most of the practical applications that are high-dimensional, linear SVMs are popularly used. The text examines applications to social and information networks. The work also discusses another popular linear classifier, the perceptron, and compares its performance with that of the SVM in different application areas.>

Support Vector Machine

Author : Fouad Sabry
Publisher : One Billion Knowledgeable
ISBN 13 :
Total Pages : 90 pages
Book Rating : 4.:/5 (661 download)

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Book Synopsis Support Vector Machine by : Fouad Sabry

Download or read book Support Vector Machine written by Fouad Sabry and published by One Billion Knowledgeable. This book was released on 2023-06-23 with total page 90 pages. Available in PDF, EPUB and Kindle. Book excerpt: What Is Support Vector Machine In the field of machine learning, support vector machines are supervised learning models that examine data for classification and regression analysis. These models come with related learning algorithms. Vladimir Vapnik and his coworkers at AT&T Bell Laboratories were responsible for its creation. Because they are founded on statistical learning frameworks or the VC theory, which was developed by Vapnik and Chervonenkis (1974), support vector machines (SVMs) are among the most accurate prediction systems. A non-probabilistic binary linear classifier is what results when an SVM training algorithm is given a series of training examples, each of which is marked as belonging to one of two categories. The algorithm then develops a model that assigns subsequent examples to either one of the two categories or neither of them. The support vector machine (SVM) allocates training examples to points in space in such a way as to maximize the difference in size between the two categories. After that, new examples are mapped into that same space, and depending on which side of the gap they fall on, a prediction is made as to which category they belong to. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Support vector machine Chapter 2: Linear classifier Chapter 3: Perceptron Chapter 4: Projection (linear algebra) Chapter 5: Linear separability Chapter 6: Kernel method Chapter 7: Sequential minimal optimization Chapter 8: Least-squares support vector machine Chapter 9: Hinge loss Chapter 10: Polynomial kernel (II) Answering the public top questions about support vector machine. (III) Real world examples for the usage of support vector machine in many fields. (IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of support vector machine' technologies. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of support vector machine.

Support Vector Machines: Theory and Applications

Author : Lipo Wang
Publisher : Springer Science & Business Media
ISBN 13 : 9783540243885
Total Pages : 456 pages
Book Rating : 4.2/5 (438 download)

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Book Synopsis Support Vector Machines: Theory and Applications by : Lipo Wang

Download or read book Support Vector Machines: Theory and Applications written by Lipo Wang and published by Springer Science & Business Media. This book was released on 2005-06-21 with total page 456 pages. Available in PDF, EPUB and Kindle. Book excerpt: The support vector machine (SVM) has become one of the standard tools for machine learning and data mining. This carefully edited volume presents the state of the art of the mathematical foundation of SVM in statistical learning theory, as well as novel algorithms and applications. Support Vector Machines provides a selection of numerous real-world applications, such as bioinformatics, text categorization, pattern recognition, and object detection, written by leading experts in their respective fields.

Perceptrons

Author : Fouad Sabry
Publisher : One Billion Knowledgeable
ISBN 13 :
Total Pages : 161 pages
Book Rating : 4.:/5 (661 download)

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Book Synopsis Perceptrons by : Fouad Sabry

Download or read book Perceptrons written by Fouad Sabry and published by One Billion Knowledgeable. This book was released on 2023-06-25 with total page 161 pages. Available in PDF, EPUB and Kindle. Book excerpt: What Is Perceptrons The perceptron is a technique for supervised learning of binary classifiers that is used in the field of machine learning. A function known as a binary classifier is one that can determine whether or not an input, which is often portrayed by a vector of numbers, is a member of a particular category. It is a kind of linear classifier, which means that it is a classification method that forms its predictions on the basis of a linear predictor function by combining a set of weights with the feature vector. In other words, it creates its predictions based on a linear predictor function. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Perceptron Chapter 2: Supervised learning Chapter 3: Support vector machine Chapter 4: Linear classifier Chapter 5: Pattern recognition Chapter 6: Artificial neuron Chapter 7: Hopfield network Chapter 8: Backpropagation Chapter 9: Feedforward neural network Chapter 10: Multilayer perceptron (II) Answering the public top questions about perceptrons. (III) Real world examples for the usage of perceptrons in many fields. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of perceptrons. What Is Artificial Intelligence Series The Artificial Intelligence eBook series provides comprehensive coverage in over 200 topics. Each ebook covers a specific Artificial Intelligence topic in depth, written by experts in the field. The series aims to give readers a thorough understanding of the concepts, techniques, history and applications of artificial intelligence. Topics covered include machine learning, deep learning, neural networks, computer vision, natural language processing, robotics, ethics and more. The ebooks are written for professionals, students, and anyone interested in learning about the latest developments in this rapidly advancing field. The Artificial Intelligence eBook series provides an in-depth yet accessible exploration, from the fundamental concepts to the state-of-the-art research. With over 200 volumes, readers gain a thorough grounding in all aspects of Artificial Intelligence. The ebooks are designed to build knowledge systematically, with later volumes building on the foundations laid by earlier ones. This comprehensive series is an indispensable resource for anyone seeking to develop expertise in artificial intelligence.

Pattern Recognition and Image Analysis

Author : Francisco José Perales
Publisher : Springer Science & Business Media
ISBN 13 : 9783540402176
Total Pages : 1194 pages
Book Rating : 4.4/5 (21 download)

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Book Synopsis Pattern Recognition and Image Analysis by : Francisco José Perales

Download or read book Pattern Recognition and Image Analysis written by Francisco José Perales and published by Springer Science & Business Media. This book was released on 2003-05-22 with total page 1194 pages. Available in PDF, EPUB and Kindle. Book excerpt: The refereed proceedings of the First Iberial Conference on Pattern Recognition and Image Analysis, IbPria 2003, held in Puerto de Andratx, Mallorca, Spain in June 2003. The 130 revised papers presented were carefully reviewed and selected from 185 full papers submitted. All current aspects of ongoing research in computer vision, image processing, pattern recognition, and speech recognition are addressed.

Knowledge Discovery with Support Vector Machines

Author : Lutz H. Hamel
Publisher : John Wiley & Sons
ISBN 13 : 1118211030
Total Pages : 211 pages
Book Rating : 4.1/5 (182 download)

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Book Synopsis Knowledge Discovery with Support Vector Machines by : Lutz H. Hamel

Download or read book Knowledge Discovery with Support Vector Machines written by Lutz H. Hamel and published by John Wiley & Sons. This book was released on 2011-09-20 with total page 211 pages. Available in PDF, EPUB and Kindle. Book excerpt: An easy-to-follow introduction to support vector machines This book provides an in-depth, easy-to-follow introduction to support vector machines drawing only from minimal, carefully motivated technical and mathematical background material. It begins with a cohesive discussion of machine learning and goes on to cover: Knowledge discovery environments Describing data mathematically Linear decision surfaces and functions Perceptron learning Maximum margin classifiers Support vector machines Elements of statistical learning theory Multi-class classification Regression with support vector machines Novelty detection Complemented with hands-on exercises, algorithm descriptions, and data sets, Knowledge Discovery with Support Vector Machines is an invaluable textbook for advanced undergraduate and graduate courses. It is also an excellent tutorial on support vector machines for professionals who are pursuing research in machine learning and related areas.

Support Vector Machines Applications

Author : Yunqian Ma
Publisher : Springer Science & Business Media
ISBN 13 : 3319023004
Total Pages : 306 pages
Book Rating : 4.3/5 (19 download)

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Book Synopsis Support Vector Machines Applications by : Yunqian Ma

Download or read book Support Vector Machines Applications written by Yunqian Ma and published by Springer Science & Business Media. This book was released on 2014-02-12 with total page 306 pages. Available in PDF, EPUB and Kindle. Book excerpt: Support vector machines (SVM) have both a solid mathematical background and practical applications. This book focuses on the recent advances and applications of the SVM, such as image processing, medical practice, computer vision, and pattern recognition, machine learning, applied statistics, and artificial intelligence. The aim of this book is to create a comprehensive source on support vector machine applications.

Efficient Learning Machines

Author : Mariette Awad
Publisher : Apress
ISBN 13 : 1430259906
Total Pages : 263 pages
Book Rating : 4.4/5 (32 download)

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Book Synopsis Efficient Learning Machines by : Mariette Awad

Download or read book Efficient Learning Machines written by Mariette Awad and published by Apress. This book was released on 2015-04-27 with total page 263 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning techniques provide cost-effective alternatives to traditional methods for extracting underlying relationships between information and data and for predicting future events by processing existing information to train models. Efficient Learning Machines explores the major topics of machine learning, including knowledge discovery, classifications, genetic algorithms, neural networking, kernel methods, and biologically-inspired techniques. Mariette Awad and Rahul Khanna’s synthetic approach weaves together the theoretical exposition, design principles, and practical applications of efficient machine learning. Their experiential emphasis, expressed in their close analysis of sample algorithms throughout the book, aims to equip engineers, students of engineering, and system designers to design and create new and more efficient machine learning systems. Readers of Efficient Learning Machines will learn how to recognize and analyze the problems that machine learning technology can solve for them, how to implement and deploy standard solutions to sample problems, and how to design new systems and solutions. Advances in computing performance, storage, memory, unstructured information retrieval, and cloud computing have coevolved with a new generation of machine learning paradigms and big data analytics, which the authors present in the conceptual context of their traditional precursors. Awad and Khanna explore current developments in the deep learning techniques of deep neural networks, hierarchical temporal memory, and cortical algorithms. Nature suggests sophisticated learning techniques that deploy simple rules to generate highly intelligent and organized behaviors with adaptive, evolutionary, and distributed properties. The authors examine the most popular biologically-inspired algorithms, together with a sample application to distributed datacenter management. They also discuss machine learning techniques for addressing problems of multi-objective optimization in which solutions in real-world systems are constrained and evaluated based on how well they perform with respect to multiple objectives in aggregate. Two chapters on support vector machines and their extensions focus on recent improvements to the classification and regression techniques at the core of machine learning.

An Introduction to Support Vector Machines and Other Kernel-based Learning Methods

Author : Nello Cristianini
Publisher : Cambridge University Press
ISBN 13 : 1139643630
Total Pages : 208 pages
Book Rating : 4.1/5 (396 download)

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Book Synopsis An Introduction to Support Vector Machines and Other Kernel-based Learning Methods by : Nello Cristianini

Download or read book An Introduction to Support Vector Machines and Other Kernel-based Learning Methods written by Nello Cristianini and published by Cambridge University Press. This book was released on 2000-03-23 with total page 208 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is the first comprehensive introduction to Support Vector Machines (SVMs), a generation learning system based on recent advances in statistical learning theory. SVMs deliver state-of-the-art performance in real-world applications such as text categorisation, hand-written character recognition, image classification, biosequences analysis, etc., and are now established as one of the standard tools for machine learning and data mining. Students will find the book both stimulating and accessible, while practitioners will be guided smoothly through the material required for a good grasp of the theory and its applications. The concepts are introduced gradually in accessible and self-contained stages, while the presentation is rigorous and thorough. Pointers to relevant literature and web sites containing software ensure that it forms an ideal starting point for further study. Equally, the book and its associated web site will guide practitioners to updated literature, new applications, and on-line software.

Learning and Soft Computing

Author : Vojislav Kecman
Publisher : MIT Press
ISBN 13 : 9780262112550
Total Pages : 556 pages
Book Rating : 4.1/5 (125 download)

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Book Synopsis Learning and Soft Computing by : Vojislav Kecman

Download or read book Learning and Soft Computing written by Vojislav Kecman and published by MIT Press. This book was released on 2001 with total page 556 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook provides a thorough introduction to the field of learning from experimental data and soft computing. Support vector machines (SVM) and neural networks (NN) are the mathematical structures, or models, that underlie learning, while fuzzy logic systems (FLS) enable us to embed structured human knowledge into workable algorithms. The book assumes that it is not only useful, but necessary, to treat SVM, NN, and FLS as parts of a connected whole. Throughout, the theory and algorithms are illustrated by practical examples, as well as by problem sets and simulated experiments. This approach enables the reader to develop SVM, NN, and FLS in addition to understanding them. The book also presents three case studies: on NN-based control, financial time series analysis, and computer graphics. A solutions manual and all of the MATLAB programs needed for the simulated experiments are available.

Data Mining: Foundations and Intelligent Paradigms

Author : Dawn E. Holmes
Publisher : Springer Science & Business Media
ISBN 13 : 3642232418
Total Pages : 257 pages
Book Rating : 4.6/5 (422 download)

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Book Synopsis Data Mining: Foundations and Intelligent Paradigms by : Dawn E. Holmes

Download or read book Data Mining: Foundations and Intelligent Paradigms written by Dawn E. Holmes and published by Springer Science & Business Media. This book was released on 2011-11-09 with total page 257 pages. Available in PDF, EPUB and Kindle. Book excerpt: There are many invaluable books available on data mining theory and applications. However, in compiling a volume titled “DATA MINING: Foundations and Intelligent Paradigms: Volume 2: Core Topics including Statistical, Time-Series and Bayesian Analysis” we wish to introduce some of the latest developments to a broad audience of both specialists and non-specialists in this field.

Support Vector Machines

Author : Ingo Steinwart
Publisher : Springer Science & Business Media
ISBN 13 : 0387772421
Total Pages : 611 pages
Book Rating : 4.3/5 (877 download)

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Book Synopsis Support Vector Machines by : Ingo Steinwart

Download or read book Support Vector Machines written by Ingo Steinwart and published by Springer Science & Business Media. This book was released on 2008-09-15 with total page 611 pages. Available in PDF, EPUB and Kindle. Book excerpt: Every mathematical discipline goes through three periods of development: the naive, the formal, and the critical. David Hilbert The goal of this book is to explain the principles that made support vector machines (SVMs) a successful modeling and prediction tool for a variety of applications. We try to achieve this by presenting the basic ideas of SVMs together with the latest developments and current research questions in a uni?ed style. In a nutshell, we identify at least three reasons for the success of SVMs: their ability to learn well with only a very small number of free parameters, their robustness against several types of model violations and outliers, and last but not least their computational e?ciency compared with several other methods. Although there are several roots and precursors of SVMs, these methods gained particular momentum during the last 15 years since Vapnik (1995, 1998) published his well-known textbooks on statistical learning theory with aspecialemphasisonsupportvectormachines. Sincethen,the?eldofmachine learninghaswitnessedintenseactivityinthestudyofSVMs,whichhasspread moreandmoretootherdisciplinessuchasstatisticsandmathematics. Thusit seems fair to say that several communities are currently working on support vector machines and on related kernel-based methods. Although there are many interactions between these communities, we think that there is still roomforadditionalfruitfulinteractionandwouldbegladifthistextbookwere found helpful in stimulating further research. Many of the results presented in this book have previously been scattered in the journal literature or are still under review. As a consequence, these results have been accessible only to a relativelysmallnumberofspecialists,sometimesprobablyonlytopeoplefrom one community but not the others.

Nonlinear Modeling

Author : Johan A. K. Suykens
Publisher : Springer Science & Business Media
ISBN 13 : 9780792381952
Total Pages : 284 pages
Book Rating : 4.3/5 (819 download)

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Book Synopsis Nonlinear Modeling by : Johan A. K. Suykens

Download or read book Nonlinear Modeling written by Johan A. K. Suykens and published by Springer Science & Business Media. This book was released on 1998-06-30 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt: This collection of eight contributions presents advanced black-box techniques for nonlinear modeling. The methods discussed include neural nets and related model structures for nonlinear system identification, enhanced multi-stream Kalman filter training for recurrent networks, the support vector method of function estimation, parametric density estimation for the classification of acoustic feature vectors in speech recognition, wavelet based modeling of nonlinear systems, nonlinear identification based on fuzzy models, statistical learning in control and matrix theory, and nonlinear time- series analysis. The volume concludes with the results of a time- series prediction competition held at a July 1998 workshop in Belgium. Annotation copyrighted by Book News, Inc., Portland, OR.

Least Squares Support Vector Machines

Author : Johan A. K. Suykens
Publisher : World Scientific
ISBN 13 : 9789812381514
Total Pages : 318 pages
Book Rating : 4.3/5 (815 download)

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Book Synopsis Least Squares Support Vector Machines by : Johan A. K. Suykens

Download or read book Least Squares Support Vector Machines written by Johan A. K. Suykens and published by World Scientific. This book was released on 2002 with total page 318 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on Least Squares Support Vector Machines (LS-SVMs) which are reformulations to standard SVMs. LS-SVMs are closely related to regularization networks and Gaussian processes but additionally emphasize and exploit primal-dual interpretations from optimization theory. The authors explain the natural links between LS-SVM classifiers and kernel Fisher discriminant analysis. Bayesian inference of LS-SVM models is discussed, together with methods for imposing spareness and employing robust statistics. The framework is further extended towards unsupervised learning by considering PCA analysis and its kernel version as a one-class modelling problem. This leads to new primal-dual support vector machine formulations for kernel PCA and kernel CCA analysis. Furthermore, LS-SVM formulations are given for recurrent networks and control. In general, support vector machines may pose heavy computational challenges for large data sets. For this purpose, a method of fixed size LS-SVM is proposed where the estimation is done in the primal space in relation to a Nystrom sampling with active selection of support vectors. The methods are illustrated with several examples.

Twin Support Vector Machines

Author : Jayadeva
Publisher : Springer
ISBN 13 : 3319461869
Total Pages : 221 pages
Book Rating : 4.3/5 (194 download)

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Book Synopsis Twin Support Vector Machines by : Jayadeva

Download or read book Twin Support Vector Machines written by Jayadeva and published by Springer. This book was released on 2016-10-12 with total page 221 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a systematic and focused study of the various aspects of twin support vector machines (TWSVM) and related developments for classification and regression. In addition to presenting most of the basic models of TWSVM and twin support vector regression (TWSVR) available in the literature, it also discusses the important and challenging applications of this new machine learning methodology. A chapter on “Additional Topics” has been included to discuss kernel optimization and support tensor machine topics, which are comparatively new but have great potential in applications. It is primarily written for graduate students and researchers in the area of machine learning and related topics in computer science, mathematics, electrical engineering, management science and finance.

Deep Learning and the Game of Go

Author : Kevin Ferguson
Publisher : Simon and Schuster
ISBN 13 : 1638354014
Total Pages : 611 pages
Book Rating : 4.6/5 (383 download)

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Book Synopsis Deep Learning and the Game of Go by : Kevin Ferguson

Download or read book Deep Learning and the Game of Go written by Kevin Ferguson and published by Simon and Schuster. This book was released on 2019-01-06 with total page 611 pages. Available in PDF, EPUB and Kindle. Book excerpt: Summary Deep Learning and the Game of Go teaches you how to apply the power of deep learning to complex reasoning tasks by building a Go-playing AI. After exposing you to the foundations of machine and deep learning, you'll use Python to build a bot and then teach it the rules of the game. Foreword by Thore Graepel, DeepMind Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology The ancient strategy game of Go is an incredible case study for AI. In 2016, a deep learning-based system shocked the Go world by defeating a world champion. Shortly after that, the upgraded AlphaGo Zero crushed the original bot by using deep reinforcement learning to master the game. Now, you can learn those same deep learning techniques by building your own Go bot! About the Book Deep Learning and the Game of Go introduces deep learning by teaching you to build a Go-winning bot. As you progress, you'll apply increasingly complex training techniques and strategies using the Python deep learning library Keras. You'll enjoy watching your bot master the game of Go, and along the way, you'll discover how to apply your new deep learning skills to a wide range of other scenarios! What's inside Build and teach a self-improving game AI Enhance classical game AI systems with deep learning Implement neural networks for deep learning About the Reader All you need are basic Python skills and high school-level math. No deep learning experience required. About the Author Max Pumperla and Kevin Ferguson are experienced deep learning specialists skilled in distributed systems and data science. Together, Max and Kevin built the open source bot BetaGo. Table of Contents PART 1 - FOUNDATIONS Toward deep learning: a machine-learning introduction Go as a machine-learning problem Implementing your first Go bot PART 2 - MACHINE LEARNING AND GAME AI Playing games with tree search Getting started with neural networks Designing a neural network for Go data Learning from data: a deep-learning bot Deploying bots in the wild Learning by practice: reinforcement learning Reinforcement learning with policy gradients Reinforcement learning with value methods Reinforcement learning with actor-critic methods PART 3 - GREATER THAN THE SUM OF ITS PARTS AlphaGo: Bringing it all together AlphaGo Zero: Integrating tree search with reinforcement learning

Multi-Layer Perceptrons and Support Vector Machines for Detection Problems with Low False Alarm Requirements

Author :
Publisher :
ISBN 13 :
Total Pages : 64 pages
Book Rating : 4.:/5 (727 download)

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Book Synopsis Multi-Layer Perceptrons and Support Vector Machines for Detection Problems with Low False Alarm Requirements by :

Download or read book Multi-Layer Perceptrons and Support Vector Machines for Detection Problems with Low False Alarm Requirements written by and published by . This book was released on 2007 with total page 64 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this project, the basic problem is to automatically separate test samples into one of two categories: clean or corrupt. This type of classification problem is known as a two-class classification problem or detection problem. In what follows, we refer to clean examples as negative examples and corrupt examples as positive examples. In a detection problem, a classifier decision on any one sample can be grouped into one of four decision categories: true negative, true positive, false negative and false positive. These four categories are illustrated by Table 1. True negatives and true positives are cases where the classifier has made the correct decision. False positives are cases where the classifier decides positive when the true nature of the sample was negative, and false negatives are cases where the classifier decides negative when the sample was actually positive. To evaluate the performance of a classifier, we run the classifier on all the samples of a data set and then count all the instances of true negatives, true positives, false negatives, and false positives. All of the performance metrics in this report are then formed from a combination of these four basic decision categories.