The Nature of Statistical Learning Theory:
Saved in:
| Main Author: | |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
New York, NY
Springer New York
1995
|
| Subjects: | |
| Online Access: | Volltext |
| Item Description: | The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization. It considers learning from the general point of view of function estimation based on empirical data. Omitting proofs and technical details, the author concentrates on discussing the main results of learning theory and their connections to fundamental problems in statistics. These include: - the general setting of learning problems and the general model of minimizing the risk functional from empirical data - a comprehensive analysis of the empirical risk minimization principle and shows how this allows for the construction of necessary and sufficient conditions for consistency - non-asymptotic bounds for the risk achieved using the empirical risk minimization principle - principles for controlling the generalization ability of learning machines using small sample sizes - introducing a new type of universal learning machine that controls the generalization ability |
| Physical Description: | 1 Online-Ressource (XV, 188 p) |
| ISBN: | 9781475724400 9781475724424 |
| DOI: | 10.1007/978-1-4757-2440-0 |
Staff View
MARC
| LEADER | 00000nmm a2200000zc 4500 | ||
|---|---|---|---|
| 001 | BV042421342 | ||
| 003 | DE-604 | ||
| 005 | 00000000000000.0 | ||
| 007 | cr|uuu---uuuuu | ||
| 008 | 150317s1995 |||| o||u| ||||||eng d | ||
| 020 | |a 9781475724400 |c Online |9 978-1-4757-2440-0 | ||
| 020 | |a 9781475724424 |c Print |9 978-1-4757-2442-4 | ||
| 024 | 7 | |a 10.1007/978-1-4757-2440-0 |2 doi | |
| 035 | |a (OCoLC)905485685 | ||
| 035 | |a (DE-599)BVBBV042421342 | ||
| 040 | |a DE-604 |b ger |e aacr | ||
| 041 | 0 | |a eng | |
| 049 | |a DE-384 |a DE-703 |a DE-91 |a DE-634 | ||
| 082 | 0 | |a 519.2 |2 23 | |
| 084 | |a MAT 000 |2 stub | ||
| 100 | 1 | |a Vapnik, Vladimir N. |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a The Nature of Statistical Learning Theory |c by Vladimir N. Vapnik |
| 264 | 1 | |a New York, NY |b Springer New York |c 1995 | |
| 300 | |a 1 Online-Ressource (XV, 188 p) | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b c |2 rdamedia | ||
| 338 | |b cr |2 rdacarrier | ||
| 500 | |a The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization. It considers learning from the general point of view of function estimation based on empirical data. Omitting proofs and technical details, the author concentrates on discussing the main results of learning theory and their connections to fundamental problems in statistics. These include: - the general setting of learning problems and the general model of minimizing the risk functional from empirical data - a comprehensive analysis of the empirical risk minimization principle and shows how this allows for the construction of necessary and sufficient conditions for consistency - non-asymptotic bounds for the risk achieved using the empirical risk minimization principle - principles for controlling the generalization ability of learning machines using small sample sizes - introducing a new type of universal learning machine that controls the generalization ability | ||
| 650 | 4 | |a Mathematics | |
| 650 | 4 | |a Artificial intelligence | |
| 650 | 4 | |a Distribution (Probability theory) | |
| 650 | 4 | |a Statistics | |
| 650 | 4 | |a Probability Theory and Stochastic Processes | |
| 650 | 4 | |a Statistics, general | |
| 650 | 4 | |a Artificial Intelligence (incl. Robotics) | |
| 650 | 4 | |a Künstliche Intelligenz | |
| 650 | 4 | |a Mathematik | |
| 650 | 4 | |a Statistik | |
| 650 | 0 | 7 | |a Statistik |0 (DE-588)4056995-0 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Lernender Automat |0 (DE-588)4167398-0 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Neuronales Netz |0 (DE-588)4226127-2 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Mathematische Lerntheorie |0 (DE-588)4169103-9 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Denken |0 (DE-588)4011450-8 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Lerntheorie |0 (DE-588)4114402-8 |2 gnd |9 rswk-swf |
| 689 | 0 | 0 | |a Lerntheorie |0 (DE-588)4114402-8 |D s |
| 689 | 0 | 1 | |a Statistik |0 (DE-588)4056995-0 |D s |
| 689 | 0 | |8 1\p |5 DE-604 | |
| 689 | 1 | 0 | |a Mathematische Lerntheorie |0 (DE-588)4169103-9 |D s |
| 689 | 1 | |8 2\p |5 DE-604 | |
| 689 | 2 | 0 | |a Lernender Automat |0 (DE-588)4167398-0 |D s |
| 689 | 2 | |8 3\p |5 DE-604 | |
| 689 | 3 | 0 | |a Neuronales Netz |0 (DE-588)4226127-2 |D s |
| 689 | 3 | |8 4\p |5 DE-604 | |
| 689 | 4 | 0 | |a Denken |0 (DE-588)4011450-8 |D s |
| 689 | 4 | |8 5\p |5 DE-604 | |
| 856 | 4 | 0 | |u https://doi.org/10.1007/978-1-4757-2440-0 |x Verlag |3 Volltext |
| 912 | |a ZDB-2-SMA |a ZDB-2-BAE | ||
| 940 | 1 | |q ZDB-2-SMA_Archive | |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-027856759 | ||
| 883 | 1 | |8 1\p |a cgwrk |d 20201028 |q DE-101 |u https://d-nb.info/provenance/plan#cgwrk | |
| 883 | 1 | |8 2\p |a cgwrk |d 20201028 |q DE-101 |u https://d-nb.info/provenance/plan#cgwrk | |
| 883 | 1 | |8 3\p |a cgwrk |d 20201028 |q DE-101 |u https://d-nb.info/provenance/plan#cgwrk | |
| 883 | 1 | |8 4\p |a cgwrk |d 20201028 |q DE-101 |u https://d-nb.info/provenance/plan#cgwrk | |
| 883 | 1 | |8 5\p |a cgwrk |d 20201028 |q DE-101 |u https://d-nb.info/provenance/plan#cgwrk | |
Record in the Search Index
| _version_ | 1804153094272974848 |
|---|---|
| any_adam_object | |
| author | Vapnik, Vladimir N. |
| author_facet | Vapnik, Vladimir N. |
| author_role | aut |
| author_sort | Vapnik, Vladimir N. |
| author_variant | v n v vn vnv |
| building | Verbundindex |
| bvnumber | BV042421342 |
| classification_tum | MAT 000 |
| collection | ZDB-2-SMA ZDB-2-BAE |
| ctrlnum | (OCoLC)905485685 (DE-599)BVBBV042421342 |
| dewey-full | 519.2 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 519 - Probabilities and applied mathematics |
| dewey-raw | 519.2 |
| dewey-search | 519.2 |
| dewey-sort | 3519.2 |
| dewey-tens | 510 - Mathematics |
| discipline | Mathematik |
| doi_str_mv | 10.1007/978-1-4757-2440-0 |
| format | Electronic eBook |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>03782nmm a2200733zc 4500</leader><controlfield tag="001">BV042421342</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">00000000000000.0</controlfield><controlfield tag="007">cr|uuu---uuuuu</controlfield><controlfield tag="008">150317s1995 |||| o||u| ||||||eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781475724400</subfield><subfield code="c">Online</subfield><subfield code="9">978-1-4757-2440-0</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781475724424</subfield><subfield code="c">Print</subfield><subfield code="9">978-1-4757-2442-4</subfield></datafield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/978-1-4757-2440-0</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)905485685</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)BVBBV042421342</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield><subfield code="e">aacr</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-384</subfield><subfield code="a">DE-703</subfield><subfield code="a">DE-91</subfield><subfield code="a">DE-634</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">519.2</subfield><subfield code="2">23</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">MAT 000</subfield><subfield code="2">stub</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Vapnik, Vladimir N.</subfield><subfield code="e">Verfasser</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">The Nature of Statistical Learning Theory</subfield><subfield code="c">by Vladimir N. Vapnik</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">New York, NY</subfield><subfield code="b">Springer New York</subfield><subfield code="c">1995</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource (XV, 188 p)</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization. It considers learning from the general point of view of function estimation based on empirical data. Omitting proofs and technical details, the author concentrates on discussing the main results of learning theory and their connections to fundamental problems in statistics. These include: - the general setting of learning problems and the general model of minimizing the risk functional from empirical data - a comprehensive analysis of the empirical risk minimization principle and shows how this allows for the construction of necessary and sufficient conditions for consistency - non-asymptotic bounds for the risk achieved using the empirical risk minimization principle - principles for controlling the generalization ability of learning machines using small sample sizes - introducing a new type of universal learning machine that controls the generalization ability</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Mathematics</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Artificial intelligence</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Distribution (Probability theory)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Statistics</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Probability Theory and Stochastic Processes</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Statistics, general</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Artificial Intelligence (incl. Robotics)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Künstliche Intelligenz</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Mathematik</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Statistik</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Statistik</subfield><subfield code="0">(DE-588)4056995-0</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Lernender Automat</subfield><subfield code="0">(DE-588)4167398-0</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Neuronales Netz</subfield><subfield code="0">(DE-588)4226127-2</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Mathematische Lerntheorie</subfield><subfield code="0">(DE-588)4169103-9</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Denken</subfield><subfield code="0">(DE-588)4011450-8</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Lerntheorie</subfield><subfield code="0">(DE-588)4114402-8</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="689" ind1="0" ind2="0"><subfield code="a">Lerntheorie</subfield><subfield code="0">(DE-588)4114402-8</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="1"><subfield code="a">Statistik</subfield><subfield code="0">(DE-588)4056995-0</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2=" "><subfield code="8">1\p</subfield><subfield code="5">DE-604</subfield></datafield><datafield tag="689" ind1="1" ind2="0"><subfield code="a">Mathematische Lerntheorie</subfield><subfield code="0">(DE-588)4169103-9</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="1" ind2=" "><subfield code="8">2\p</subfield><subfield code="5">DE-604</subfield></datafield><datafield tag="689" ind1="2" ind2="0"><subfield code="a">Lernender Automat</subfield><subfield code="0">(DE-588)4167398-0</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="2" ind2=" "><subfield code="8">3\p</subfield><subfield code="5">DE-604</subfield></datafield><datafield tag="689" ind1="3" ind2="0"><subfield code="a">Neuronales Netz</subfield><subfield code="0">(DE-588)4226127-2</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="3" ind2=" "><subfield code="8">4\p</subfield><subfield code="5">DE-604</subfield></datafield><datafield tag="689" ind1="4" ind2="0"><subfield code="a">Denken</subfield><subfield code="0">(DE-588)4011450-8</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="4" ind2=" "><subfield code="8">5\p</subfield><subfield code="5">DE-604</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1007/978-1-4757-2440-0</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-2-SMA</subfield><subfield code="a">ZDB-2-BAE</subfield></datafield><datafield tag="940" ind1="1" ind2=" "><subfield code="q">ZDB-2-SMA_Archive</subfield></datafield><datafield tag="999" ind1=" " ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-027856759</subfield></datafield><datafield tag="883" ind1="1" ind2=" "><subfield code="8">1\p</subfield><subfield code="a">cgwrk</subfield><subfield code="d">20201028</subfield><subfield code="q">DE-101</subfield><subfield code="u">https://d-nb.info/provenance/plan#cgwrk</subfield></datafield><datafield tag="883" ind1="1" ind2=" "><subfield code="8">2\p</subfield><subfield code="a">cgwrk</subfield><subfield code="d">20201028</subfield><subfield code="q">DE-101</subfield><subfield code="u">https://d-nb.info/provenance/plan#cgwrk</subfield></datafield><datafield tag="883" ind1="1" ind2=" "><subfield code="8">3\p</subfield><subfield code="a">cgwrk</subfield><subfield code="d">20201028</subfield><subfield code="q">DE-101</subfield><subfield code="u">https://d-nb.info/provenance/plan#cgwrk</subfield></datafield><datafield tag="883" ind1="1" ind2=" "><subfield code="8">4\p</subfield><subfield code="a">cgwrk</subfield><subfield code="d">20201028</subfield><subfield code="q">DE-101</subfield><subfield code="u">https://d-nb.info/provenance/plan#cgwrk</subfield></datafield><datafield tag="883" ind1="1" ind2=" "><subfield code="8">5\p</subfield><subfield code="a">cgwrk</subfield><subfield code="d">20201028</subfield><subfield code="q">DE-101</subfield><subfield code="u">https://d-nb.info/provenance/plan#cgwrk</subfield></datafield></record></collection> |
| id | DE-604.BV042421342 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T01:21:08Z |
| institution | BVB |
| isbn | 9781475724400 9781475724424 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-027856759 |
| oclc_num | 905485685 |
| open_access_boolean | |
| owner | DE-384 DE-703 DE-91 DE-BY-TUM DE-634 |
| owner_facet | DE-384 DE-703 DE-91 DE-BY-TUM DE-634 |
| physical | 1 Online-Ressource (XV, 188 p) |
| psigel | ZDB-2-SMA ZDB-2-BAE ZDB-2-SMA_Archive |
| publishDate | 1995 |
| publishDateSearch | 1995 |
| publishDateSort | 1995 |
| publisher | Springer New York |
| record_format | marc |
| spelling | Vapnik, Vladimir N. Verfasser aut The Nature of Statistical Learning Theory by Vladimir N. Vapnik New York, NY Springer New York 1995 1 Online-Ressource (XV, 188 p) txt rdacontent c rdamedia cr rdacarrier The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization. It considers learning from the general point of view of function estimation based on empirical data. Omitting proofs and technical details, the author concentrates on discussing the main results of learning theory and their connections to fundamental problems in statistics. These include: - the general setting of learning problems and the general model of minimizing the risk functional from empirical data - a comprehensive analysis of the empirical risk minimization principle and shows how this allows for the construction of necessary and sufficient conditions for consistency - non-asymptotic bounds for the risk achieved using the empirical risk minimization principle - principles for controlling the generalization ability of learning machines using small sample sizes - introducing a new type of universal learning machine that controls the generalization ability Mathematics Artificial intelligence Distribution (Probability theory) Statistics Probability Theory and Stochastic Processes Statistics, general Artificial Intelligence (incl. Robotics) Künstliche Intelligenz Mathematik Statistik Statistik (DE-588)4056995-0 gnd rswk-swf Lernender Automat (DE-588)4167398-0 gnd rswk-swf Neuronales Netz (DE-588)4226127-2 gnd rswk-swf Mathematische Lerntheorie (DE-588)4169103-9 gnd rswk-swf Denken (DE-588)4011450-8 gnd rswk-swf Lerntheorie (DE-588)4114402-8 gnd rswk-swf Lerntheorie (DE-588)4114402-8 s Statistik (DE-588)4056995-0 s 1\p DE-604 Mathematische Lerntheorie (DE-588)4169103-9 s 2\p DE-604 Lernender Automat (DE-588)4167398-0 s 3\p DE-604 Neuronales Netz (DE-588)4226127-2 s 4\p DE-604 Denken (DE-588)4011450-8 s 5\p DE-604 https://doi.org/10.1007/978-1-4757-2440-0 Verlag Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 2\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 3\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 4\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 5\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Vapnik, Vladimir N. The Nature of Statistical Learning Theory Mathematics Artificial intelligence Distribution (Probability theory) Statistics Probability Theory and Stochastic Processes Statistics, general Artificial Intelligence (incl. Robotics) Künstliche Intelligenz Mathematik Statistik Statistik (DE-588)4056995-0 gnd Lernender Automat (DE-588)4167398-0 gnd Neuronales Netz (DE-588)4226127-2 gnd Mathematische Lerntheorie (DE-588)4169103-9 gnd Denken (DE-588)4011450-8 gnd Lerntheorie (DE-588)4114402-8 gnd |
| subject_GND | (DE-588)4056995-0 (DE-588)4167398-0 (DE-588)4226127-2 (DE-588)4169103-9 (DE-588)4011450-8 (DE-588)4114402-8 |
| title | The Nature of Statistical Learning Theory |
| title_auth | The Nature of Statistical Learning Theory |
| title_exact_search | The Nature of Statistical Learning Theory |
| title_full | The Nature of Statistical Learning Theory by Vladimir N. Vapnik |
| title_fullStr | The Nature of Statistical Learning Theory by Vladimir N. Vapnik |
| title_full_unstemmed | The Nature of Statistical Learning Theory by Vladimir N. Vapnik |
| title_short | The Nature of Statistical Learning Theory |
| title_sort | the nature of statistical learning theory |
| topic | Mathematics Artificial intelligence Distribution (Probability theory) Statistics Probability Theory and Stochastic Processes Statistics, general Artificial Intelligence (incl. Robotics) Künstliche Intelligenz Mathematik Statistik Statistik (DE-588)4056995-0 gnd Lernender Automat (DE-588)4167398-0 gnd Neuronales Netz (DE-588)4226127-2 gnd Mathematische Lerntheorie (DE-588)4169103-9 gnd Denken (DE-588)4011450-8 gnd Lerntheorie (DE-588)4114402-8 gnd |
| topic_facet | Mathematics Artificial intelligence Distribution (Probability theory) Statistics Probability Theory and Stochastic Processes Statistics, general Artificial Intelligence (incl. Robotics) Künstliche Intelligenz Mathematik Statistik Lernender Automat Neuronales Netz Mathematische Lerntheorie Denken Lerntheorie |
| url | https://doi.org/10.1007/978-1-4757-2440-0 |
| work_keys_str_mv | AT vapnikvladimirn thenatureofstatisticallearningtheory |