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....
Gespeichert in:
| 1. Verfasser: | |
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| Weitere Verfasser: | |
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Cambridge, Mass. :
MIT Press,
©2002.
|
| Schriftenreihe: | Adaptive computation and machine learning.
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| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | 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. |
| Beschreibung: | 1 online resource (xviii, 626 pages) : illustrations |
| Bibliographie: | Includes bibliographical references (pages 591-616) and index. |
| ISBN: | 9780262256933 0262256932 0585477590 9780585477596 9780262194754 0262194759 |
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| 100 | 1 | |a Schölkopf, Bernhard. | |
| 245 | 1 | 0 | |a Learning with kernels : |b support vector machines, regularization, optimization, and beyond / |c Bernhard Schölkopf, Alexander J. Smola. |
| 260 | |a Cambridge, Mass. : |b MIT Press, |c ©2002. | ||
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| 504 | |a Includes bibliographical references (pages 591-616) and index. | ||
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| 505 | 0 | |a Series Foreword; Preface; 1 -- A Tutorial Introduction; I -- Concepts and Tools; 2 -- Kernels; 3 -- Risk and Loss Functions; 4 -- Regularization; 5 -- Elements of Statistical Learning Theory; 6 -- Optimization; II -- Support Vector Machines; 7 -- Pattern Recognition; 8 -- Single-Class Problems: Quantile Estimation and Novelty Detection; 9 -- Regression Estimation; 10 -- Implementation; 11 -- Incorporating Invariances; 12 -- Learning Theory Revisited; III -- Kernel Methods; 13 -- Designing Kernels; 14 -- Kernel Feature Extraction; 15 -- Kernel Fisher Discriminant; 16 -- Bayesian Kernel Methods. | |
| 505 | 8 | |a 17 -- Regularized Principal Manifolds18 -- Pre-Images and Reduced Set Methods; A -- Addenda; B -- Mathematical Prerequisites; References; Index; Notation and Symbols. | |
| 520 | |a 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. | ||
| 546 | |a English. | ||
| 650 | 0 | |a Machine learning. |0 http://id.loc.gov/authorities/subjects/sh85079324 | |
| 650 | 0 | |a Algorithms. |0 http://id.loc.gov/authorities/subjects/sh85003487 | |
| 650 | 0 | |a Kernel functions. |0 http://id.loc.gov/authorities/subjects/sh85072061 | |
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| 700 | 1 | |a Smola, Alexander J. | |
| 758 | |i has work: |a Learning with Kernels (Text) |1 https://id.oclc.org/worldcat/entity/E39PCG4DrM6Bydf6fyPQpqxRXb |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 776 | 0 | 8 | |i Print version: |a Schölkopf, Bernhard. |t Learning with kernels. |d Cambridge, Mass. : MIT Press, ©2002 |z 0262194759 |w (DLC) 2001095750 |w (OCoLC)48970254 |
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| contents | Series Foreword; Preface; 1 -- A Tutorial Introduction; I -- Concepts and Tools; 2 -- Kernels; 3 -- Risk and Loss Functions; 4 -- Regularization; 5 -- Elements of Statistical Learning Theory; 6 -- Optimization; II -- Support Vector Machines; 7 -- Pattern Recognition; 8 -- Single-Class Problems: Quantile Estimation and Novelty Detection; 9 -- Regression Estimation; 10 -- Implementation; 11 -- Incorporating Invariances; 12 -- Learning Theory Revisited; III -- Kernel Methods; 13 -- Designing Kernels; 14 -- Kernel Feature Extraction; 15 -- Kernel Fisher Discriminant; 16 -- Bayesian Kernel Methods. 17 -- Regularized Principal Manifolds18 -- Pre-Images and Reduced Set Methods; A -- Addenda; B -- Mathematical Prerequisites; References; Index; Notation and Symbols. |
| ctrlnum | (OCoLC)53833203 |
| dewey-full | 006.3/1 |
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| dewey-ones | 006 - Special computer methods |
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| dewey-search | 006.3/1 |
| dewey-sort | 16.3 11 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| format | Electronic eBook |
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| id | ZDB-4-EBU-ocm53833203 |
| illustrated | Illustrated |
| indexdate | 2024-11-26T14:48:56Z |
| institution | BVB |
| isbn | 9780262256933 0262256932 0585477590 9780585477596 9780262194754 0262194759 |
| language | English |
| oclc_num | 53833203 |
| open_access_boolean | |
| owner | MAIN DE-863 DE-BY-FWS |
| owner_facet | MAIN DE-863 DE-BY-FWS |
| physical | 1 online resource (xviii, 626 pages) : illustrations |
| psigel | ZDB-4-EBU |
| publishDate | 2002 |
| publishDateSearch | 2002 |
| publishDateSort | 2002 |
| publisher | MIT Press, |
| record_format | marc |
| series | Adaptive computation and machine learning. |
| series2 | Adaptive computation and machine learning |
| 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 resource (xviii, 626 pages) : illustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier text file rdaft Adaptive computation and machine learning Includes bibliographical references (pages 591-616) and index. Print version record. Series Foreword; Preface; 1 -- A Tutorial Introduction; I -- Concepts and Tools; 2 -- Kernels; 3 -- Risk and Loss Functions; 4 -- Regularization; 5 -- Elements of Statistical Learning Theory; 6 -- Optimization; II -- Support Vector Machines; 7 -- Pattern Recognition; 8 -- Single-Class Problems: Quantile Estimation and Novelty Detection; 9 -- Regression Estimation; 10 -- Implementation; 11 -- Incorporating Invariances; 12 -- Learning Theory Revisited; III -- Kernel Methods; 13 -- Designing Kernels; 14 -- Kernel Feature Extraction; 15 -- Kernel Fisher Discriminant; 16 -- Bayesian Kernel Methods. 17 -- Regularized Principal Manifolds18 -- Pre-Images and Reduced Set Methods; A -- Addenda; B -- Mathematical Prerequisites; References; Index; Notation and Symbols. 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. English. Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Algorithms. http://id.loc.gov/authorities/subjects/sh85003487 Kernel functions. http://id.loc.gov/authorities/subjects/sh85072061 Algorithms https://id.nlm.nih.gov/mesh/D000465 Machine Learning https://id.nlm.nih.gov/mesh/D000069550 Apprentissage automatique. Algorithmes. Noyaux (Mathématiques) algorithms. aat COMPUTERS Enterprise Applications Business Intelligence Tools. bisacsh COMPUTERS Intelligence (AI) & Semantics. bisacsh Algorithms fast Kernel functions fast Machine learning fast Machine-learning. gtt Vectorcomputers. (NL-LeOCL)095992553 gtt COMPUTER SCIENCE/Machine Learning & Neural Networks Smola, Alexander J. has work: Learning with Kernels (Text) https://id.oclc.org/worldcat/entity/E39PCG4DrM6Bydf6fyPQpqxRXb https://id.oclc.org/worldcat/ontology/hasWork Print version: Schölkopf, Bernhard. Learning with kernels. Cambridge, Mass. : MIT Press, ©2002 0262194759 (DLC) 2001095750 (OCoLC)48970254 Adaptive computation and machine learning. http://id.loc.gov/authorities/names/n97066095 FWS01 ZDB-4-EBU FWS_PDA_EBU https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=78092 Volltext |
| spellingShingle | Schölkopf, Bernhard Learning with kernels : support vector machines, regularization, optimization, and beyond / Adaptive computation and machine learning. Series Foreword; Preface; 1 -- A Tutorial Introduction; I -- Concepts and Tools; 2 -- Kernels; 3 -- Risk and Loss Functions; 4 -- Regularization; 5 -- Elements of Statistical Learning Theory; 6 -- Optimization; II -- Support Vector Machines; 7 -- Pattern Recognition; 8 -- Single-Class Problems: Quantile Estimation and Novelty Detection; 9 -- Regression Estimation; 10 -- Implementation; 11 -- Incorporating Invariances; 12 -- Learning Theory Revisited; III -- Kernel Methods; 13 -- Designing Kernels; 14 -- Kernel Feature Extraction; 15 -- Kernel Fisher Discriminant; 16 -- Bayesian Kernel Methods. 17 -- Regularized Principal Manifolds18 -- Pre-Images and Reduced Set Methods; A -- Addenda; B -- Mathematical Prerequisites; References; Index; Notation and Symbols. Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Algorithms. http://id.loc.gov/authorities/subjects/sh85003487 Kernel functions. http://id.loc.gov/authorities/subjects/sh85072061 Algorithms https://id.nlm.nih.gov/mesh/D000465 Machine Learning https://id.nlm.nih.gov/mesh/D000069550 Apprentissage automatique. Algorithmes. Noyaux (Mathématiques) algorithms. aat COMPUTERS Enterprise Applications Business Intelligence Tools. bisacsh COMPUTERS Intelligence (AI) & Semantics. bisacsh Algorithms fast Kernel functions fast Machine learning fast Machine-learning. gtt Vectorcomputers. (NL-LeOCL)095992553 gtt |
| subject_GND | http://id.loc.gov/authorities/subjects/sh85079324 http://id.loc.gov/authorities/subjects/sh85003487 http://id.loc.gov/authorities/subjects/sh85072061 https://id.nlm.nih.gov/mesh/D000465 https://id.nlm.nih.gov/mesh/D000069550 (NL-LeOCL)095992553 |
| 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 / |
| topic | Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Algorithms. http://id.loc.gov/authorities/subjects/sh85003487 Kernel functions. http://id.loc.gov/authorities/subjects/sh85072061 Algorithms https://id.nlm.nih.gov/mesh/D000465 Machine Learning https://id.nlm.nih.gov/mesh/D000069550 Apprentissage automatique. Algorithmes. Noyaux (Mathématiques) algorithms. aat COMPUTERS Enterprise Applications Business Intelligence Tools. bisacsh COMPUTERS Intelligence (AI) & Semantics. bisacsh Algorithms fast Kernel functions fast Machine learning fast Machine-learning. gtt Vectorcomputers. (NL-LeOCL)095992553 gtt |
| topic_facet | Machine learning. Algorithms. Kernel functions. Algorithms Machine Learning Apprentissage automatique. Algorithmes. Noyaux (Mathématiques) algorithms. COMPUTERS Enterprise Applications Business Intelligence Tools. COMPUTERS Intelligence (AI) & Semantics. Kernel functions Machine learning Machine-learning. Vectorcomputers. |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=78092 |
| work_keys_str_mv | AT scholkopfbernhard learningwithkernelssupportvectormachinesregularizationoptimizationandbeyond AT smolaalexanderj learningwithkernelssupportvectormachinesregularizationoptimizationandbeyond |