Advances in learning theory :: methods, models and applications /
This text details advances in learning theory that relate to problems studied in neural networks, machine learning, mathematics and statistics.
Gespeichert in:
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| Weitere Verfasser: | |
| Format: | Elektronisch Tagungsbericht E-Book |
| Sprache: | English |
| Veröffentlicht: |
Amsterdam ; Washington, DC : Tokyo :
IOS Press ; Ohmsha,
©2003.
|
| Schriftenreihe: | NATO science series. Computer and systems sciences ;
v. 190. |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | This text details advances in learning theory that relate to problems studied in neural networks, machine learning, mathematics and statistics. |
| Beschreibung: | "Proceedings of the NATO Advanced Study Institute on Learning Theory and Practice, 8-19 July 2002, Leuven, Belgium"--Title page verso "Published in cooperation with NATO Scientific Affairs Division." |
| Beschreibung: | 1 online resource (xxi, 415 pages) : illustrations |
| Format: | Master and use copy. Digital master created according to Benchmark for Faithful Digital Reproductions of Monographs and Serials, Version 1. Digital Library Federation, December 2002. |
| Bibliographie: | Includes bibliographical references and indexes. |
| ISBN: | 1586033417 9781586033415 427490587X 9784274905872 1417511397 9781417511396 1601294018 9781601294012 |
| ISSN: | 1387-6694 ; |
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| 245 | 1 | 0 | |a Advances in learning theory : |b methods, models and applications / |c edited by Johan Suykens [and others]. |
| 260 | |a Amsterdam ; |a Washington, DC : |b IOS Press ; |a Tokyo : |b Ohmsha, |c ©2003. | ||
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| 490 | 1 | |a NATO science series. Series III, Computer and systems sciences, |x 1387-6694 ; |v v. 190 | |
| 500 | |a "Proceedings of the NATO Advanced Study Institute on Learning Theory and Practice, 8-19 July 2002, Leuven, Belgium"--Title page verso | ||
| 500 | |a "Published in cooperation with NATO Scientific Affairs Division." | ||
| 504 | |a Includes bibliographical references and indexes. | ||
| 505 | 0 | |a Cover; Title page; Preface; Organizing committee; List of chapter contributors; Contents; 1 An Overview of Statistical Learning Theory; 2 Best Choices for Regularization Parameters in Learning Theory: On the Bias-Variance Problem; 3 Cucker Smale Learning Theory in Besov Spaces; 4 High-dimensional Approximation by Neural Networks; 5 Functional Learning through Kernels; 6 Leave-one-out Error and Stability of Learning Algorithms with Applications; 7 Regularized Least-Squares Classification; 8 Support Vector Machines: Least Squares Approaches and Extensions. | |
| 520 | |a This text details advances in learning theory that relate to problems studied in neural networks, machine learning, mathematics and statistics. | ||
| 588 | 0 | |a Print version record. | |
| 506 | |3 Use copy |f Restrictions unspecified |2 star |5 MiAaHDL | ||
| 533 | |a Electronic reproduction. |b [Place of publication not identified] : |c HathiTrust Digital Library, |d 2011. |5 MiAaHDL | ||
| 538 | |a Master and use copy. Digital master created according to Benchmark for Faithful Digital Reproductions of Monographs and Serials, Version 1. Digital Library Federation, December 2002. |u http://purl.oclc.org/DLF/benchrepro0212 |5 MiAaHDL | ||
| 583 | 1 | |a digitized |c 2011 |h HathiTrust Digital Library |l committed to preserve |2 pda |5 MiAaHDL | |
| 650 | 0 | |a Computational learning theory |v Congresses. | |
| 650 | 0 | |a Machine learning |x Mathematical models |v Congresses. | |
| 650 | 6 | |a Théorie de l'apprentissage informatique |v Congrès. | |
| 650 | 6 | |a Apprentissage automatique |x Modèles mathématiques |v Congrès. | |
| 650 | 7 | |a COMPUTERS |x Enterprise Applications |x Business Intelligence Tools. |2 bisacsh | |
| 650 | 7 | |a COMPUTERS |x Intelligence (AI) & Semantics. |2 bisacsh | |
| 650 | 7 | |a Computational learning theory |2 fast | |
| 650 | 7 | |a Machine learning |x Mathematical models |2 fast | |
| 655 | 7 | |a Conference papers and proceedings |2 fast | |
| 700 | 1 | |a Suykens, Johan A. K. | |
| 758 | |i has work: |a Advances in learning theory (Text) |1 https://id.oclc.org/worldcat/entity/E39PCGGpX8C4b7YdFYGQpJjHmd |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 776 | 0 | 8 | |i Print version: |a NATO Advanced Study Institute on Learning Theory and Practice (2002 : Louvain, Belgium). |t Advances in learning theory. |d Amsterdam ; Washington, DC : IOS Press ; Tokyo : Ohmsha, ©2003 |z 1586033417 |z 427490587X |w (DLC) 2003103695 |w (OCoLC)53136779 |
| 830 | 0 | |a NATO science series. |n Series III, |p Computer and systems sciences ; |v v. 190. |x 1387-6694 | |
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| _version_ | 1816796895743311872 |
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| author2 | Suykens, Johan A. K. |
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| author_corporate | NATO Advanced Study Institute on Learning Theory and Practice Louvain, Belgium |
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| author_facet | Suykens, Johan A. K. NATO Advanced Study Institute on Learning Theory and Practice Louvain, Belgium |
| author_sort | NATO Advanced Study Institute on Learning Theory and Practice Louvain, Belgium |
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| contents | Cover; Title page; Preface; Organizing committee; List of chapter contributors; Contents; 1 An Overview of Statistical Learning Theory; 2 Best Choices for Regularization Parameters in Learning Theory: On the Bias-Variance Problem; 3 Cucker Smale Learning Theory in Besov Spaces; 4 High-dimensional Approximation by Neural Networks; 5 Functional Learning through Kernels; 6 Leave-one-out Error and Stability of Learning Algorithms with Applications; 7 Regularized Least-Squares Classification; 8 Support Vector Machines: Least Squares Approaches and Extensions. |
| ctrlnum | (OCoLC)70720234 |
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| dewey-ones | 006 - Special computer methods |
| dewey-raw | 006.3/1 |
| dewey-search | 006.3/1 |
| dewey-sort | 16.3 11 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| format | Electronic Conference Proceeding eBook |
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| genre | Conference papers and proceedings fast |
| genre_facet | Conference papers and proceedings |
| id | ZDB-4-EBU-ocm70720234 |
| illustrated | Illustrated |
| indexdate | 2024-11-26T14:48:57Z |
| institution | BVB |
| isbn | 1586033417 9781586033415 427490587X 9784274905872 1417511397 9781417511396 1601294018 9781601294012 |
| issn | 1387-6694 ; |
| language | English |
| oclc_num | 70720234 |
| open_access_boolean | |
| owner | MAIN DE-863 DE-BY-FWS |
| owner_facet | MAIN DE-863 DE-BY-FWS |
| physical | 1 online resource (xxi, 415 pages) : illustrations |
| psigel | ZDB-4-EBU |
| publishDate | 2003 |
| publishDateSearch | 2003 |
| publishDateSort | 2003 |
| publisher | IOS Press ; Ohmsha, |
| record_format | marc |
| series | NATO science series. Computer and systems sciences ; |
| series2 | NATO science series. Series III, Computer and systems sciences, |
| spelling | NATO Advanced Study Institute on Learning Theory and Practice (2002 : Louvain, Belgium) Advances in learning theory : methods, models and applications / edited by Johan Suykens [and others]. Amsterdam ; Washington, DC : IOS Press ; Tokyo : Ohmsha, ©2003. 1 online resource (xxi, 415 pages) : illustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier data file NATO science series. Series III, Computer and systems sciences, 1387-6694 ; v. 190 "Proceedings of the NATO Advanced Study Institute on Learning Theory and Practice, 8-19 July 2002, Leuven, Belgium"--Title page verso "Published in cooperation with NATO Scientific Affairs Division." Includes bibliographical references and indexes. Cover; Title page; Preface; Organizing committee; List of chapter contributors; Contents; 1 An Overview of Statistical Learning Theory; 2 Best Choices for Regularization Parameters in Learning Theory: On the Bias-Variance Problem; 3 Cucker Smale Learning Theory in Besov Spaces; 4 High-dimensional Approximation by Neural Networks; 5 Functional Learning through Kernels; 6 Leave-one-out Error and Stability of Learning Algorithms with Applications; 7 Regularized Least-Squares Classification; 8 Support Vector Machines: Least Squares Approaches and Extensions. This text details advances in learning theory that relate to problems studied in neural networks, machine learning, mathematics and statistics. Print version record. Use copy Restrictions unspecified star MiAaHDL Electronic reproduction. [Place of publication not identified] : HathiTrust Digital Library, 2011. MiAaHDL Master and use copy. Digital master created according to Benchmark for Faithful Digital Reproductions of Monographs and Serials, Version 1. Digital Library Federation, December 2002. http://purl.oclc.org/DLF/benchrepro0212 MiAaHDL digitized 2011 HathiTrust Digital Library committed to preserve pda MiAaHDL Computational learning theory Congresses. Machine learning Mathematical models Congresses. Théorie de l'apprentissage informatique Congrès. Apprentissage automatique Modèles mathématiques Congrès. COMPUTERS Enterprise Applications Business Intelligence Tools. bisacsh COMPUTERS Intelligence (AI) & Semantics. bisacsh Computational learning theory fast Machine learning Mathematical models fast Conference papers and proceedings fast Suykens, Johan A. K. has work: Advances in learning theory (Text) https://id.oclc.org/worldcat/entity/E39PCGGpX8C4b7YdFYGQpJjHmd https://id.oclc.org/worldcat/ontology/hasWork Print version: NATO Advanced Study Institute on Learning Theory and Practice (2002 : Louvain, Belgium). Advances in learning theory. Amsterdam ; Washington, DC : IOS Press ; Tokyo : Ohmsha, ©2003 1586033417 427490587X (DLC) 2003103695 (OCoLC)53136779 NATO science series. Series III, Computer and systems sciences ; v. 190. 1387-6694 FWS01 ZDB-4-EBU FWS_PDA_EBU https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=110129 Volltext |
| spellingShingle | Advances in learning theory : methods, models and applications / NATO science series. Computer and systems sciences ; Cover; Title page; Preface; Organizing committee; List of chapter contributors; Contents; 1 An Overview of Statistical Learning Theory; 2 Best Choices for Regularization Parameters in Learning Theory: On the Bias-Variance Problem; 3 Cucker Smale Learning Theory in Besov Spaces; 4 High-dimensional Approximation by Neural Networks; 5 Functional Learning through Kernels; 6 Leave-one-out Error and Stability of Learning Algorithms with Applications; 7 Regularized Least-Squares Classification; 8 Support Vector Machines: Least Squares Approaches and Extensions. Computational learning theory Congresses. Machine learning Mathematical models Congresses. Théorie de l'apprentissage informatique Congrès. Apprentissage automatique Modèles mathématiques Congrès. COMPUTERS Enterprise Applications Business Intelligence Tools. bisacsh COMPUTERS Intelligence (AI) & Semantics. bisacsh Computational learning theory fast Machine learning Mathematical models fast |
| title | Advances in learning theory : methods, models and applications / |
| title_auth | Advances in learning theory : methods, models and applications / |
| title_exact_search | Advances in learning theory : methods, models and applications / |
| title_full | Advances in learning theory : methods, models and applications / edited by Johan Suykens [and others]. |
| title_fullStr | Advances in learning theory : methods, models and applications / edited by Johan Suykens [and others]. |
| title_full_unstemmed | Advances in learning theory : methods, models and applications / edited by Johan Suykens [and others]. |
| title_short | Advances in learning theory : |
| title_sort | advances in learning theory methods models and applications |
| title_sub | methods, models and applications / |
| topic | Computational learning theory Congresses. Machine learning Mathematical models Congresses. Théorie de l'apprentissage informatique Congrès. Apprentissage automatique Modèles mathématiques Congrès. COMPUTERS Enterprise Applications Business Intelligence Tools. bisacsh COMPUTERS Intelligence (AI) & Semantics. bisacsh Computational learning theory fast Machine learning Mathematical models fast |
| topic_facet | Computational learning theory Congresses. Machine learning Mathematical models Congresses. Théorie de l'apprentissage informatique Congrès. Apprentissage automatique Modèles mathématiques Congrès. COMPUTERS Enterprise Applications Business Intelligence Tools. COMPUTERS Intelligence (AI) & Semantics. Computational learning theory Machine learning Mathematical models Conference papers and proceedings |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=110129 |
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