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Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond (Adaptive Computation and Machine Learning)

Schlkopf, Bernhard; Smola, Alexander J.

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9780262194754: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond (Adaptive Computation and Machine Learning)

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.

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About the Author:

Bernhard Schölkopf is Professor and Director at the Max Planck Institute for Biological Cybernetics in Tübingen, Germany. He is coauthor of Learning with Kernels (2002) and is a coeditor of Advances in Kernel Methods: Support Vector Learning (1998), Advances in Large-Margin Classifiers (2000), and Kernel Methods in Computational Biology (2004), all published by the MIT Press.

Alexander J. Smola is Senior Principal Researcher and Machine Learning Program Leader at National ICT Australia/Australian National University, Canberra.

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Book Description MIT Press Ltd. Hardback. Book Condition: new. BRAND NEW, Learning with Kernels: Support Vector Machines, Regularization, Optimization and beyond, Bernhard Scholkopf, Alexander J. Smola, 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. Bookseller Inventory # B9780262194754

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Book Description MIT Press Ltd. Book Condition: New. 2001. 1st. Hardcover. A comprehensive introduction to Support Vector Machines and related kernel methods. Series: Adaptive Computation and Machine Learning Series. Num Pages: 648 pages, 138 illus. BIC Classification: UN; UYA. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly; (UU) Undergraduate. Dimension: 255 x 211 x 38. Weight in Grams: 1530. . . . . . from our Bookshop. Bookseller Inventory # V9780262194754

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Book Description MIT Press Ltd, United States, 2002. Hardback. Book Condition: New. 256 x 210 mm. Language: English Brand New Book. 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. Bookseller Inventory # AAU9780262194754

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Book Description The MIT Press, 2001. Book Condition: New. Brand New, Unread Copy in Perfect Condition. A+ Customer Service! Summary: "Interesting and original. Learning with Kernels will make a fine textbook on thissubject."--Grace Wahba, Bascom Professor of Statistics, University of Wisconsin Madison. Bookseller Inventory # ABE_book_new_0262194759

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Book Description MIT Press Ltd. Book Condition: New. 2001. 1st. Hardcover. A comprehensive introduction to Support Vector Machines and related kernel methods. Series: Adaptive Computation and Machine Learning Series. Num Pages: 648 pages, 138 illus. BIC Classification: UN; UYA. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly; (UU) Undergraduate. Dimension: 255 x 211 x 38. Weight in Grams: 1530. . . . . . Books ship from the US and Ireland. Bookseller Inventory # V9780262194754

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Book Description MIT Press Ltd, United States, 2002. Hardback. Book Condition: New. 256 x 210 mm. Language: English Brand New Book. 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. Bookseller Inventory # AAU9780262194754

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