Learning with kernels: support vector machines, regularization, optimization, and beyond
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....
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| Format: | eBook |
| Language: | English |
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Cambridge, Mass.
MIT Press
©2002
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| Series: | Adaptive computation and machine learning
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| Links: | https://doi.org/10.7551/mitpress/4175.001.0001?locatt=mode:legacy |
| Summary: | 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. |
| Physical Description: | 1 Online-Ressource (xviii, 626 Seiten) Illustrationen |
| ISBN: | 0262256932 0585477590 9780262256933 9780585477596 |
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| spelling | Schölkopf, Bernhard Learning with kernels support vector machines, regularization, optimization, and beyond Bernhard Schölkopf, Alexander J. Smola Cambridge, Mass. MIT Press ©2002 1 Online-Ressource (xviii, 626 Seiten) Illustrationen txt c cr 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. Smola, Alexander J. Erscheint auch als Druck-Ausgabe 0262194759 Erscheint auch als Druck-Ausgabe 9780262194754 |
| spellingShingle | Schölkopf, Bernhard Learning with kernels support vector machines, regularization, optimization, and beyond |
| title | Learning with kernels support vector machines, regularization, optimization, and beyond |
| title_auth | Learning with kernels support vector machines, regularization, optimization, and beyond |
| title_exact_search | Learning with kernels support vector machines, regularization, optimization, and beyond |
| title_full | Learning with kernels support vector machines, regularization, optimization, and beyond Bernhard Schölkopf, Alexander J. Smola |
| title_fullStr | Learning with kernels support vector machines, regularization, optimization, and beyond Bernhard Schölkopf, Alexander J. Smola |
| title_full_unstemmed | Learning with kernels support vector machines, regularization, optimization, and beyond Bernhard Schölkopf, Alexander J. Smola |
| title_short | Learning with kernels |
| title_sort | learning with kernels support vector machines regularization optimization and beyond |
| title_sub | support vector machines, regularization, optimization, and beyond |
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