Learning with Fractional Orthogonal Kernel Classifiers in Support Vector Machines PDF Download
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Author: Jamal Amani Rad
Publisher: Springer Nature
ISBN: 9811965536
Category : Mathematics
Languages : en
Pages : 312
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Book Description
This book contains select chapters on support vector algorithms from different perspectives, including mathematical background, properties of various kernel functions, and several applications. The main focus of this book is on orthogonal kernel functions, and the properties of the classical kernel functions—Chebyshev, Legendre, Gegenbauer, and Jacobi—are reviewed in some chapters. Moreover, the fractional form of these kernel functions is introduced in the same chapters, and for ease of use for these kernel functions, a tutorial on a Python package named ORSVM is presented. The book also exhibits a variety of applications for support vector algorithms, and in addition to the classification, these algorithms along with the introduced kernel functions are utilized for solving ordinary, partial, integro, and fractional differential equations. On the other hand, nowadays, the real-time and big data applications of support vector algorithms are growing. Consequently, the Compute Unified Device Architecture (CUDA) parallelizing the procedure of support vector algorithms based on orthogonal kernel functions is presented. The book sheds light on how to use support vector algorithms based on orthogonal kernel functions in different situations and gives a significant perspective to all machine learning and scientific machine learning researchers all around the world to utilize fractional orthogonal kernel functions in their pattern recognition or scientific computing problems.
Author: Jamal Amani Rad
Publisher: Springer Nature
ISBN: 9811965536
Category : Mathematics
Languages : en
Pages : 312
Get Book
Book Description
This book contains select chapters on support vector algorithms from different perspectives, including mathematical background, properties of various kernel functions, and several applications. The main focus of this book is on orthogonal kernel functions, and the properties of the classical kernel functions—Chebyshev, Legendre, Gegenbauer, and Jacobi—are reviewed in some chapters. Moreover, the fractional form of these kernel functions is introduced in the same chapters, and for ease of use for these kernel functions, a tutorial on a Python package named ORSVM is presented. The book also exhibits a variety of applications for support vector algorithms, and in addition to the classification, these algorithms along with the introduced kernel functions are utilized for solving ordinary, partial, integro, and fractional differential equations. On the other hand, nowadays, the real-time and big data applications of support vector algorithms are growing. Consequently, the Compute Unified Device Architecture (CUDA) parallelizing the procedure of support vector algorithms based on orthogonal kernel functions is presented. The book sheds light on how to use support vector algorithms based on orthogonal kernel functions in different situations and gives a significant perspective to all machine learning and scientific machine learning researchers all around the world to utilize fractional orthogonal kernel functions in their pattern recognition or scientific computing problems.
Author: Bernhard Scholkopf
Publisher: MIT Press
ISBN: 0262536579
Category : Computers
Languages : en
Pages : 645
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Book Description
A comprehensive introduction to Support Vector Machines and related kernel methods. In the 1990s, a new type of learning algorithm was developed, based on results from statistical learning theory: the Support Vector Machine (SVM). This gave rise to a new class of theoretically elegant learning machines that use a central concept of SVMs—-kernels—for a number of learning tasks. Kernel machines provide a modular framework that can be adapted to different tasks and domains by the choice of the kernel function and the base algorithm. They are replacing neural networks in a variety of fields, including engineering, information retrieval, and bioinformatics. Learning with Kernels provides an introduction to SVMs and related kernel methods. Although the book begins with the basics, it also includes the latest research. It provides all of the concepts necessary to enable a reader equipped with some basic mathematical knowledge to enter the world of machine learning using theoretically well-founded yet easy-to-use kernel algorithms and to understand and apply the powerful algorithms that have been developed over the last few years.
Author: Ralf Herbrich
Publisher: MIT Press
ISBN: 9780262263047
Category : Computers
Languages : en
Pages : 402
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Book Description
An overview of the theory and application of kernel classification methods. Linear classifiers in kernel spaces have emerged as a major topic within the field of machine learning. The kernel technique takes the linear classifier—a limited, but well-established and comprehensively studied model—and extends its applicability to a wide range of nonlinear pattern-recognition tasks such as natural language processing, machine vision, and biological sequence analysis. This book provides the first comprehensive overview of both the theory and algorithms of kernel classifiers, including the most recent developments. It begins by describing the major algorithmic advances: kernel perceptron learning, kernel Fisher discriminants, support vector machines, relevance vector machines, Gaussian processes, and Bayes point machines. Then follows a detailed introduction to learning theory, including VC and PAC-Bayesian theory, data-dependent structural risk minimization, and compression bounds. Throughout, the book emphasizes the interaction between theory and algorithms: how learning algorithms work and why. The book includes many examples, complete pseudo code of the algorithms presented, and an extensive source code library.
Author: Nello Cristianini
Publisher: Cambridge University Press
ISBN: 9780521780193
Category : Computers
Languages : en
Pages : 216
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Book Description
This is a comprehensive introduction to Support Vector Machines, a generation learning system based on advances in statistical learning theory.
Author: Fouad Sabry
Publisher: One Billion Knowledgeable
ISBN:
Category : Computers
Languages : en
Pages : 90
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Book Description
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.
Author: Colin Campbell
Publisher: Morgan & Claypool Publishers
ISBN: 1608456161
Category : Computers
Languages : en
Pages : 97
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Book Description
Support Vectors Machines have become a well established tool within machine learning. They work well in practice and have now been used across a wide range of applications from recognizing hand-written digits, to face identification, text categorisation, bioinformatics, and database marketing. In this book we give an introductory overview of this subject. We start with a simple Support Vector Machine for performing binary classification before considering multi-class classification and learning in the presence of noise. We show that this framework can be extended to many other scenarios such as prediction with real-valued outputs, novelty detection and the handling of complex output structures such as parse trees. Finally, we give an overview of the main types of kernels which are used in practice and how to learn and make predictions from multiple types of input data. Table of Contents: Support Vector Machines for Classification / Kernel-based Models / Learning with Kernels
Author: M.N. Murty
Publisher: Springer
ISBN: 3319410636
Category : Computers
Languages : en
Pages : 103
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Book Description
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.>
Author: Ralf Herbrich
Publisher: Mit Press
ISBN: 9780262083065
Category : Computers
Languages : en
Pages : 364
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Book Description
An overview of the theory and application of kernel classification methods.
Author: Alexander J. Smola
Publisher: MIT Press
ISBN: 9780262194488
Category : Computers
Languages : en
Pages : 436
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Book Description
The book provides an overview of recent developments in large margin classifiers, examines connections with other methods (e.g., Bayesian inference), and identifies strengths and weaknesses of the method, as well as directions for future research. The concept of large margins is a unifying principle for the analysis of many different approaches to the classification of data from examples, including boosting, mathematical programming, neural networks, and support vector machines. The fact that it is the margin, or confidence level, of a classification--that is, a scale parameter--rather than a raw training error that matters has become a key tool for dealing with classifiers. This book shows how this idea applies to both the theoretical analysis and the design of algorithms. The book provides an overview of recent developments in large margin classifiers, examines connections with other methods (e.g., Bayesian inference), and identifies strengths and weaknesses of the method, as well as directions for future research. Among the contributors are Manfred Opper, Vladimir Vapnik, and Grace Wahba.
Author: S. Y. Kung
Publisher: Cambridge University Press
ISBN: 110702496X
Category : Computers
Languages : en
Pages : 617
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Book Description
Covering the fundamentals of kernel-based learning theory, this is an essential resource for graduate students and professionals in computer science.
