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....
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Schölkopf, Bernhard [verfasserIn] Smola, Alexander J. [mitwirkender] |
|---|
| Format: |
E-Book |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
Cambridge, Massachusetts: MIT Press ; c2002 |
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| Schlagwörter: |
Maschinelles Lernen / Support-Vektor-Maschine Maschinelles Lernen / Kernel, Informatik / Support-Vektor-Maschine |
|---|---|
| Schlagwörter: |
| Systematik: |
|
|---|
| Anmerkung: |
Includes bibliographical references (p. [591]-616) and index. - Description based on PDF viewed 12/23/2015 |
|---|---|
| Umfang: |
1 PDF (xviii, 626 pages) ; illustrations. |
| Beschreibung: |
Mode of access: World Wide Web. |
| Weitere Ausgabe: |
Erscheint auch als Druck-Ausgabe Schölkopf, Bernhard, 1968 -: Learning with kernels - Cambridge, Massachusetts : MIT Press, 2002 |
|---|
| Reihe: |
Adaptive computation and machine learning series |
|---|
| Links: | |
|---|---|
| ISBN: |
978-0-262-25693-3 |
| Katalog-ID: |
1727350464 |
|---|
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| 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. | ||
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9780262256933 ebook 978-0-262-25693-3 (DE-627)1727350464 (DE-599)KEP055118526 (OCoLC)1196123268 (MITPRESS)6267332 (EBP)055118526 DE-627 ger DE-627 rda eng 006.3/1 21 006.3/1 22 ST 300 SEPA rvk (DE-625)rvk/143650: ST 278 SEPA rvk (DE-625)rvk/143644: ST 304 SEPA rvk (DE-625)rvk/143653: 54.72 bkl 31.80 bkl Schölkopf, Bernhard verfasserin aut Learning with kernels support vector machines, regularization, optimization, and beyond Bernhard Schölkopf, Alexander J. Smola Cambridge, Massachusetts MIT Press c2002 1 PDF (xviii, 626 pages) illustrations. Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Adaptive computation and machine learning series Includes bibliographical references (p. [591]-616) and index. - Description based on PDF viewed 12/23/2015 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. Mode of access: World Wide Web. Machine learning Algorithms Kernel functions s (DE-588)4193754-5 (DE-627)105224782 (DE-576)21008944X Maschinelles Lernen gnd s (DE-588)4505517-8 (DE-627)245346708 (DE-576)213125757 Support-Vektor-Maschine gnd DE-101 s (DE-588)4193754-5 (DE-627)105224782 (DE-576)21008944X Maschinelles Lernen gnd s (DE-588)4338679-9 (DE-627)152412972 (DE-576)211390577 Kernel Informatik gnd s (DE-588)4505517-8 (DE-627)245346708 (DE-576)213125757 Support-Vektor-Maschine gnd (DE-627) Smola, Alexander J. mitwirkender ctb 9780262194754 Erscheint auch als Druck-Ausgabe 9780262194754 Erscheint auch als Druck-Ausgabe Schölkopf, Bernhard, 1968 - Learning with kernels Cambridge, Massachusetts : MIT Press, 2002 xviii, 626 Seiten (DE-627)345507118 0262194759 9780262194754 9780262536578 https://ieeexplore.ieee.org/book/6267332 X:MITPRESS Verlag lizenzpflichtig ZDB-37-IEM 2012 GBV_ILN_22 ISIL_DE-18 SYSFLAG_1 GBV_KXP GBV_ILN_22_i22818 GBV_ILN_23 ISIL_DE-830 GBV_ILN_100 ISIL_DE-Ma9 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_2015 ISIL_DE-93 ST 300 Allgemeines Informatik Monografien Künstliche Intelligenz Allgemeines (DE-627)1271119005 (DE-625)rvk/143650: (DE-576)201119005 ST 278 Mensch-Maschine-Kommunikation Software-Ergonomie Informatik Monografien Software und -entwicklung Mensch-Maschine-Kommunikation Software-Ergonomie (DE-627)1270879251 (DE-625)rvk/143644: (DE-576)200879251 ST 304 Automatisches Programmieren, Deduction and theorem proving, Wissensrepräsentation Informatik Monografien Künstliche Intelligenz Automatisches Programmieren, Deduction and theorem proving, Wissensrepräsentation (DE-627)1271584034 (DE-625)rvk/143653: (DE-576)201584034 54.72 Künstliche Intelligenz SEPA (DE-627)10641240X 31.80 Angewandte Mathematik SEPA (DE-627)106419005 BO 045F 006.3/1 045F 006.3/1 22 01 0018 3848476509 olrm-h228-MITIEEE zi22818 03-02-21 23 01 0830 3957242002 olr-MIT i z 23-07-21 100 01 3100 4472469111 09 --%%-- eBook MIT Press --%%-- --%%-- OLR-MIT-CEC Vervielfältigungen (z.B. Kopien, Downloads) sind nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. z 30-01-24 370 01 4370 4011223433 olr-ebook mitieee Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. i z 01-12-21 2015 01 DE-93 3740752432 00 --%%-- --%%-- p --%%-- Campuslizenz l01 18-08-20 22 01 0018 Volltextzugang Campus https://ieeexplore.ieee.org/book/6267332 22 01 0018 Nur für Angehörige der Universität Hamburg: Volltextzugang von außerhalb des Campus http://emedien.sub.uni-hamburg.de/han/ieee/ieeexplore.ieee.org/book/6267332 23 01 0830 MIT Press EBook https://ieeexplore.ieee.org/book/6267332 100 01 3100 https://ieeexplore.ieee.org/book/6267332 100 01 3100 für Uniangehörige: Zugang weltweit http://han.med.uni-magdeburg.de/han/mitvia-ieee/ieeexplore.ieee.org/book/6267332 370 01 4370 E-Book: Zugriff im HCU-Netz. Zugriff von außerhalb nur für HCU-Angehörige möglich https://ieeexplore.ieee.org/book/6267332 2015 01 DE-93 https://ieeexplore.ieee.org/book/6267332 23 01 0830 2018-01805, 2018-01806, 2018-01808 22 01 0018 olrm-h228-MITIEEE 23 01 0830 olr-MIT 100 01 3100 OLR-MIT-CEC 370 01 4370 olr-ebook mitieee 370 01 4370 2021.12.01 |
| spelling |
9780262256933 ebook 978-0-262-25693-3 (DE-627)1727350464 (DE-599)KEP055118526 (OCoLC)1196123268 (MITPRESS)6267332 (EBP)055118526 DE-627 ger DE-627 rda eng 006.3/1 21 006.3/1 22 ST 300 SEPA rvk (DE-625)rvk/143650: ST 278 SEPA rvk (DE-625)rvk/143644: ST 304 SEPA rvk (DE-625)rvk/143653: 54.72 bkl 31.80 bkl Schölkopf, Bernhard verfasserin aut Learning with kernels support vector machines, regularization, optimization, and beyond Bernhard Schölkopf, Alexander J. Smola Cambridge, Massachusetts MIT Press c2002 1 PDF (xviii, 626 pages) illustrations. Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Adaptive computation and machine learning series Includes bibliographical references (p. [591]-616) and index. - Description based on PDF viewed 12/23/2015 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. Mode of access: World Wide Web. Machine learning Algorithms Kernel functions s (DE-588)4193754-5 (DE-627)105224782 (DE-576)21008944X Maschinelles Lernen gnd s (DE-588)4505517-8 (DE-627)245346708 (DE-576)213125757 Support-Vektor-Maschine gnd DE-101 s (DE-588)4193754-5 (DE-627)105224782 (DE-576)21008944X Maschinelles Lernen gnd s (DE-588)4338679-9 (DE-627)152412972 (DE-576)211390577 Kernel Informatik gnd s (DE-588)4505517-8 (DE-627)245346708 (DE-576)213125757 Support-Vektor-Maschine gnd (DE-627) Smola, Alexander J. mitwirkender ctb 9780262194754 Erscheint auch als Druck-Ausgabe 9780262194754 Erscheint auch als Druck-Ausgabe Schölkopf, Bernhard, 1968 - Learning with kernels Cambridge, Massachusetts : MIT Press, 2002 xviii, 626 Seiten (DE-627)345507118 0262194759 9780262194754 9780262536578 https://ieeexplore.ieee.org/book/6267332 X:MITPRESS Verlag lizenzpflichtig ZDB-37-IEM 2012 GBV_ILN_22 ISIL_DE-18 SYSFLAG_1 GBV_KXP GBV_ILN_22_i22818 GBV_ILN_23 ISIL_DE-830 GBV_ILN_100 ISIL_DE-Ma9 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_2015 ISIL_DE-93 ST 300 Allgemeines Informatik Monografien Künstliche Intelligenz Allgemeines (DE-627)1271119005 (DE-625)rvk/143650: (DE-576)201119005 ST 278 Mensch-Maschine-Kommunikation Software-Ergonomie Informatik Monografien Software und -entwicklung Mensch-Maschine-Kommunikation Software-Ergonomie (DE-627)1270879251 (DE-625)rvk/143644: (DE-576)200879251 ST 304 Automatisches Programmieren, Deduction and theorem proving, Wissensrepräsentation Informatik Monografien Künstliche Intelligenz Automatisches Programmieren, Deduction and theorem proving, Wissensrepräsentation (DE-627)1271584034 (DE-625)rvk/143653: (DE-576)201584034 54.72 Künstliche Intelligenz SEPA (DE-627)10641240X 31.80 Angewandte Mathematik SEPA (DE-627)106419005 BO 045F 006.3/1 045F 006.3/1 22 01 0018 3848476509 olrm-h228-MITIEEE zi22818 03-02-21 23 01 0830 3957242002 olr-MIT i z 23-07-21 100 01 3100 4472469111 09 --%%-- eBook MIT Press --%%-- --%%-- OLR-MIT-CEC Vervielfältigungen (z.B. Kopien, Downloads) sind nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. z 30-01-24 370 01 4370 4011223433 olr-ebook mitieee Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. i z 01-12-21 2015 01 DE-93 3740752432 00 --%%-- --%%-- p --%%-- Campuslizenz l01 18-08-20 22 01 0018 Volltextzugang Campus https://ieeexplore.ieee.org/book/6267332 22 01 0018 Nur für Angehörige der Universität Hamburg: Volltextzugang von außerhalb des Campus http://emedien.sub.uni-hamburg.de/han/ieee/ieeexplore.ieee.org/book/6267332 23 01 0830 MIT Press EBook https://ieeexplore.ieee.org/book/6267332 100 01 3100 https://ieeexplore.ieee.org/book/6267332 100 01 3100 für Uniangehörige: Zugang weltweit http://han.med.uni-magdeburg.de/han/mitvia-ieee/ieeexplore.ieee.org/book/6267332 370 01 4370 E-Book: Zugriff im HCU-Netz. Zugriff von außerhalb nur für HCU-Angehörige möglich https://ieeexplore.ieee.org/book/6267332 2015 01 DE-93 https://ieeexplore.ieee.org/book/6267332 23 01 0830 2018-01805, 2018-01806, 2018-01808 22 01 0018 olrm-h228-MITIEEE 23 01 0830 olr-MIT 100 01 3100 OLR-MIT-CEC 370 01 4370 olr-ebook mitieee 370 01 4370 2021.12.01 |
| allfields_unstemmed |
9780262256933 ebook 978-0-262-25693-3 (DE-627)1727350464 (DE-599)KEP055118526 (OCoLC)1196123268 (MITPRESS)6267332 (EBP)055118526 DE-627 ger DE-627 rda eng 006.3/1 21 006.3/1 22 ST 300 SEPA rvk (DE-625)rvk/143650: ST 278 SEPA rvk (DE-625)rvk/143644: ST 304 SEPA rvk (DE-625)rvk/143653: 54.72 bkl 31.80 bkl Schölkopf, Bernhard verfasserin aut Learning with kernels support vector machines, regularization, optimization, and beyond Bernhard Schölkopf, Alexander J. Smola Cambridge, Massachusetts MIT Press c2002 1 PDF (xviii, 626 pages) illustrations. Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Adaptive computation and machine learning series Includes bibliographical references (p. [591]-616) and index. - Description based on PDF viewed 12/23/2015 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. Mode of access: World Wide Web. Machine learning Algorithms Kernel functions s (DE-588)4193754-5 (DE-627)105224782 (DE-576)21008944X Maschinelles Lernen gnd s (DE-588)4505517-8 (DE-627)245346708 (DE-576)213125757 Support-Vektor-Maschine gnd DE-101 s (DE-588)4193754-5 (DE-627)105224782 (DE-576)21008944X Maschinelles Lernen gnd s (DE-588)4338679-9 (DE-627)152412972 (DE-576)211390577 Kernel Informatik gnd s (DE-588)4505517-8 (DE-627)245346708 (DE-576)213125757 Support-Vektor-Maschine gnd (DE-627) Smola, Alexander J. mitwirkender ctb 9780262194754 Erscheint auch als Druck-Ausgabe 9780262194754 Erscheint auch als Druck-Ausgabe Schölkopf, Bernhard, 1968 - Learning with kernels Cambridge, Massachusetts : MIT Press, 2002 xviii, 626 Seiten (DE-627)345507118 0262194759 9780262194754 9780262536578 https://ieeexplore.ieee.org/book/6267332 X:MITPRESS Verlag lizenzpflichtig ZDB-37-IEM 2012 GBV_ILN_22 ISIL_DE-18 SYSFLAG_1 GBV_KXP GBV_ILN_22_i22818 GBV_ILN_23 ISIL_DE-830 GBV_ILN_100 ISIL_DE-Ma9 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_2015 ISIL_DE-93 ST 300 Allgemeines Informatik Monografien Künstliche Intelligenz Allgemeines (DE-627)1271119005 (DE-625)rvk/143650: (DE-576)201119005 ST 278 Mensch-Maschine-Kommunikation Software-Ergonomie Informatik Monografien Software und -entwicklung Mensch-Maschine-Kommunikation Software-Ergonomie (DE-627)1270879251 (DE-625)rvk/143644: (DE-576)200879251 ST 304 Automatisches Programmieren, Deduction and theorem proving, Wissensrepräsentation Informatik Monografien Künstliche Intelligenz Automatisches Programmieren, Deduction and theorem proving, Wissensrepräsentation (DE-627)1271584034 (DE-625)rvk/143653: (DE-576)201584034 54.72 Künstliche Intelligenz SEPA (DE-627)10641240X 31.80 Angewandte Mathematik SEPA (DE-627)106419005 BO 045F 006.3/1 045F 006.3/1 22 01 0018 3848476509 olrm-h228-MITIEEE zi22818 03-02-21 23 01 0830 3957242002 olr-MIT i z 23-07-21 100 01 3100 4472469111 09 --%%-- eBook MIT Press --%%-- --%%-- OLR-MIT-CEC Vervielfältigungen (z.B. 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Kein systematisches Downloaden durch Robots. i z 01-12-21 2015 01 DE-93 3740752432 00 --%%-- --%%-- p --%%-- Campuslizenz l01 18-08-20 22 01 0018 Volltextzugang Campus https://ieeexplore.ieee.org/book/6267332 22 01 0018 Nur für Angehörige der Universität Hamburg: Volltextzugang von außerhalb des Campus http://emedien.sub.uni-hamburg.de/han/ieee/ieeexplore.ieee.org/book/6267332 23 01 0830 MIT Press EBook https://ieeexplore.ieee.org/book/6267332 100 01 3100 https://ieeexplore.ieee.org/book/6267332 100 01 3100 für Uniangehörige: Zugang weltweit http://han.med.uni-magdeburg.de/han/mitvia-ieee/ieeexplore.ieee.org/book/6267332 370 01 4370 E-Book: Zugriff im HCU-Netz. Zugriff von außerhalb nur für HCU-Angehörige möglich https://ieeexplore.ieee.org/book/6267332 2015 01 DE-93 https://ieeexplore.ieee.org/book/6267332 23 01 0830 2018-01805, 2018-01806, 2018-01808 22 01 0018 olrm-h228-MITIEEE 23 01 0830 olr-MIT 100 01 3100 OLR-MIT-CEC 370 01 4370 olr-ebook mitieee 370 01 4370 2021.12.01 |
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9780262256933 ebook 978-0-262-25693-3 (DE-627)1727350464 (DE-599)KEP055118526 (OCoLC)1196123268 (MITPRESS)6267332 (EBP)055118526 DE-627 ger DE-627 rda eng 006.3/1 21 006.3/1 22 ST 300 SEPA rvk (DE-625)rvk/143650: ST 278 SEPA rvk (DE-625)rvk/143644: ST 304 SEPA rvk (DE-625)rvk/143653: 54.72 bkl 31.80 bkl Schölkopf, Bernhard verfasserin aut Learning with kernels support vector machines, regularization, optimization, and beyond Bernhard Schölkopf, Alexander J. Smola Cambridge, Massachusetts MIT Press c2002 1 PDF (xviii, 626 pages) illustrations. Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Adaptive computation and machine learning series Includes bibliographical references (p. [591]-616) and index. - Description based on PDF viewed 12/23/2015 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. Mode of access: World Wide Web. Machine learning Algorithms Kernel functions s (DE-588)4193754-5 (DE-627)105224782 (DE-576)21008944X Maschinelles Lernen gnd s (DE-588)4505517-8 (DE-627)245346708 (DE-576)213125757 Support-Vektor-Maschine gnd DE-101 s (DE-588)4193754-5 (DE-627)105224782 (DE-576)21008944X Maschinelles Lernen gnd s (DE-588)4338679-9 (DE-627)152412972 (DE-576)211390577 Kernel Informatik gnd s (DE-588)4505517-8 (DE-627)245346708 (DE-576)213125757 Support-Vektor-Maschine gnd (DE-627) Smola, Alexander J. mitwirkender ctb 9780262194754 Erscheint auch als Druck-Ausgabe 9780262194754 Erscheint auch als Druck-Ausgabe Schölkopf, Bernhard, 1968 - Learning with kernels Cambridge, Massachusetts : MIT Press, 2002 xviii, 626 Seiten (DE-627)345507118 0262194759 9780262194754 9780262536578 https://ieeexplore.ieee.org/book/6267332 X:MITPRESS Verlag lizenzpflichtig ZDB-37-IEM 2012 GBV_ILN_22 ISIL_DE-18 SYSFLAG_1 GBV_KXP GBV_ILN_22_i22818 GBV_ILN_23 ISIL_DE-830 GBV_ILN_100 ISIL_DE-Ma9 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_2015 ISIL_DE-93 ST 300 Allgemeines Informatik Monografien Künstliche Intelligenz Allgemeines (DE-627)1271119005 (DE-625)rvk/143650: (DE-576)201119005 ST 278 Mensch-Maschine-Kommunikation Software-Ergonomie Informatik Monografien Software und -entwicklung Mensch-Maschine-Kommunikation Software-Ergonomie (DE-627)1270879251 (DE-625)rvk/143644: (DE-576)200879251 ST 304 Automatisches Programmieren, Deduction and theorem proving, Wissensrepräsentation Informatik Monografien Künstliche Intelligenz Automatisches Programmieren, Deduction and theorem proving, Wissensrepräsentation (DE-627)1271584034 (DE-625)rvk/143653: (DE-576)201584034 54.72 Künstliche Intelligenz SEPA (DE-627)10641240X 31.80 Angewandte Mathematik SEPA (DE-627)106419005 BO 045F 006.3/1 045F 006.3/1 22 01 0018 3848476509 olrm-h228-MITIEEE zi22818 03-02-21 23 01 0830 3957242002 olr-MIT i z 23-07-21 100 01 3100 4472469111 09 --%%-- eBook MIT Press --%%-- --%%-- OLR-MIT-CEC Vervielfältigungen (z.B. Kopien, Downloads) sind nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. z 30-01-24 370 01 4370 4011223433 olr-ebook mitieee Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. i z 01-12-21 2015 01 DE-93 3740752432 00 --%%-- --%%-- p --%%-- Campuslizenz l01 18-08-20 22 01 0018 Volltextzugang Campus https://ieeexplore.ieee.org/book/6267332 22 01 0018 Nur für Angehörige der Universität Hamburg: Volltextzugang von außerhalb des Campus http://emedien.sub.uni-hamburg.de/han/ieee/ieeexplore.ieee.org/book/6267332 23 01 0830 MIT Press EBook https://ieeexplore.ieee.org/book/6267332 100 01 3100 https://ieeexplore.ieee.org/book/6267332 100 01 3100 für Uniangehörige: Zugang weltweit http://han.med.uni-magdeburg.de/han/mitvia-ieee/ieeexplore.ieee.org/book/6267332 370 01 4370 E-Book: Zugriff im HCU-Netz. Zugriff von außerhalb nur für HCU-Angehörige möglich https://ieeexplore.ieee.org/book/6267332 2015 01 DE-93 https://ieeexplore.ieee.org/book/6267332 23 01 0830 2018-01805, 2018-01806, 2018-01808 22 01 0018 olrm-h228-MITIEEE 23 01 0830 olr-MIT 100 01 3100 OLR-MIT-CEC 370 01 4370 olr-ebook mitieee 370 01 4370 2021.12.01 |
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9780262256933 ebook 978-0-262-25693-3 (DE-627)1727350464 (DE-599)KEP055118526 (OCoLC)1196123268 (MITPRESS)6267332 (EBP)055118526 DE-627 ger DE-627 rda eng 006.3/1 21 006.3/1 22 ST 300 SEPA rvk (DE-625)rvk/143650: ST 278 SEPA rvk (DE-625)rvk/143644: ST 304 SEPA rvk (DE-625)rvk/143653: 54.72 bkl 31.80 bkl Schölkopf, Bernhard verfasserin aut Learning with kernels support vector machines, regularization, optimization, and beyond Bernhard Schölkopf, Alexander J. Smola Cambridge, Massachusetts MIT Press c2002 1 PDF (xviii, 626 pages) illustrations. Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Adaptive computation and machine learning series Includes bibliographical references (p. [591]-616) and index. - Description based on PDF viewed 12/23/2015 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. Mode of access: World Wide Web. Machine learning Algorithms Kernel functions s (DE-588)4193754-5 (DE-627)105224782 (DE-576)21008944X Maschinelles Lernen gnd s (DE-588)4505517-8 (DE-627)245346708 (DE-576)213125757 Support-Vektor-Maschine gnd DE-101 s (DE-588)4193754-5 (DE-627)105224782 (DE-576)21008944X Maschinelles Lernen gnd s (DE-588)4338679-9 (DE-627)152412972 (DE-576)211390577 Kernel Informatik gnd s (DE-588)4505517-8 (DE-627)245346708 (DE-576)213125757 Support-Vektor-Maschine gnd (DE-627) Smola, Alexander J. mitwirkender ctb 9780262194754 Erscheint auch als Druck-Ausgabe 9780262194754 Erscheint auch als Druck-Ausgabe Schölkopf, Bernhard, 1968 - Learning with kernels Cambridge, Massachusetts : MIT Press, 2002 xviii, 626 Seiten (DE-627)345507118 0262194759 9780262194754 9780262536578 https://ieeexplore.ieee.org/book/6267332 X:MITPRESS Verlag lizenzpflichtig ZDB-37-IEM 2012 GBV_ILN_22 ISIL_DE-18 SYSFLAG_1 GBV_KXP GBV_ILN_22_i22818 GBV_ILN_23 ISIL_DE-830 GBV_ILN_100 ISIL_DE-Ma9 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_2015 ISIL_DE-93 ST 300 Allgemeines Informatik Monografien Künstliche Intelligenz Allgemeines (DE-627)1271119005 (DE-625)rvk/143650: (DE-576)201119005 ST 278 Mensch-Maschine-Kommunikation Software-Ergonomie Informatik Monografien Software und -entwicklung Mensch-Maschine-Kommunikation Software-Ergonomie (DE-627)1270879251 (DE-625)rvk/143644: (DE-576)200879251 ST 304 Automatisches Programmieren, Deduction and theorem proving, Wissensrepräsentation Informatik Monografien Künstliche Intelligenz Automatisches Programmieren, Deduction and theorem proving, Wissensrepräsentation (DE-627)1271584034 (DE-625)rvk/143653: (DE-576)201584034 54.72 Künstliche Intelligenz SEPA (DE-627)10641240X 31.80 Angewandte Mathematik SEPA (DE-627)106419005 BO 045F 006.3/1 045F 006.3/1 22 01 0018 3848476509 olrm-h228-MITIEEE zi22818 03-02-21 23 01 0830 3957242002 olr-MIT i z 23-07-21 100 01 3100 4472469111 09 --%%-- eBook MIT Press --%%-- --%%-- OLR-MIT-CEC Vervielfältigungen (z.B. 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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. Includes bibliographical references (p. [591]-616) and index. - Description based on PDF viewed 12/23/2015 |
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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. Includes bibliographical references (p. [591]-616) and index. - Description based on PDF viewed 12/23/2015 |
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Machine learning Automated learning Lernen <Künstliche Intelligenz> Algorithmisches Lernen Maschinelles Lernen Support vector machine SVM Support-Vektor-Maschine Betriebssystemkern Kernel <Informatik> |
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