Learning Kernel Classifiers - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Learning Kernel Classifiers write by Ralf Herbrich. This book was released on 2001-12-07. Learning Kernel Classifiers available in PDF, EPUB and Kindle. 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.
Learning Kernel Classifiers
Learning Kernel Classifiers - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Learning Kernel Classifiers write by Ralf Herbrich. This book was released on 2002-01. Learning Kernel Classifiers available in PDF, EPUB and Kindle. An overview of the theory and application of kernel classification methods.
Learning Kernel Classifiers
Learning Kernel Classifiers - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Learning Kernel Classifiers write by Ralf Herbrich. This book was released on 2022-11-01. Learning Kernel Classifiers available in PDF, EPUB and Kindle. 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.
Learning with Kernels
Learning with Kernels - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Learning with Kernels write by Bernhard Scholkopf. This book was released on 2018-06-05. Learning with Kernels available in PDF, EPUB and Kindle. 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.
Learning with Fractional Orthogonal Kernel Classifiers in Support Vector Machines
Learning with Fractional Orthogonal Kernel Classifiers in Support Vector Machines - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Learning with Fractional Orthogonal Kernel Classifiers in Support Vector Machines write by Jamal Amani Rad. This book was released on 2023-03-18. Learning with Fractional Orthogonal Kernel Classifiers in Support Vector Machines available in PDF, EPUB and Kindle. 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.
