An elementary introduction to statistical learning theory:
Gespeichert in:
| Beteilige Person: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
Hoboken, N.J.
Wiley
©2011
|
| Schriftenreihe: | Wiley series in probability and statistics
|
| Schlagwörter: | |
| Links: | http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=391376 |
| Beschreibung: | Introduction: Classification, Learning, Features, and Applications -- Probability -- Probability Densities -- The Pattern Recognition Problem -- The Optimal Bayes Decision Rule -- Learning from Examples -- The Nearest Neighbor Rule -- Kernel Rules -- Neural Networks: Perceptrons -- Multilayer Networks -- PAC Learning -- VC Dimension -- Infinite VC Dimension -- The Function Estimation Problem -- Learning Function Estimation -- Simplicity -- Support Vector Machines -- Boosting "A joint endeavor from leading researchers in the fields of philosophy and electrical engineering An Introduction to Statistical Learning Theory provides a broad and accessible introduction to rapidly evolving field of statistical pattern recognition and statistical learning theory. Exploring topics that are not often covered in introductory level books on statistical learning theory, including PAC learning, VC dimension, and simplicity, the authors present upper-undergraduate and graduate levels with the basic theory behind contemporary machine learning and uniquely suggest it serves as an excellent framework for philosophical thinking about inductive inference"--Back cover Includes bibliographical references and index |
| Umfang: | 1 Online-Ressource (1 volume) |
| ISBN: | 9781118023471 1118023471 9781118023433 1118023439 1283098687 9781283098687 9780470641835 9781118023464 0470641835 1118023463 |
Internformat
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Datensatz im Suchindex
| _version_ | 1818981001173401600 |
|---|---|
| any_adam_object | |
| author | Kulkarni, Sanjeev |
| author_GND | (DE-588)109851439 |
| author_facet | Kulkarni, Sanjeev |
| author_role | aut |
| author_sort | Kulkarni, Sanjeev |
| author_variant | s k sk |
| building | Verbundindex |
| bvnumber | BV042961309 |
| collection | ZDB-4-EBA ZDB-4-EBU |
| ctrlnum | (OCoLC)729724626 (DE-599)BVBBV042961309 |
| dewey-full | 006.3/1 |
| dewey-hundreds | 000 - Computer science, information, general works |
| 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 eBook |
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| id | DE-604.BV042961309 |
| illustrated | Not Illustrated |
| indexdate | 2024-12-20T17:24:22Z |
| institution | BVB |
| isbn | 9781118023471 1118023471 9781118023433 1118023439 1283098687 9781283098687 9780470641835 9781118023464 0470641835 1118023463 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-028387176 |
| oclc_num | 729724626 |
| open_access_boolean | |
| owner | DE-1046 DE-1047 |
| owner_facet | DE-1046 DE-1047 |
| physical | 1 Online-Ressource (1 volume) |
| psigel | ZDB-4-EBA ZDB-4-EBU FAW_PDA_EBA FLA_PDA_EBU |
| publishDate | 2011 |
| publishDateSearch | 2011 |
| publishDateSort | 2011 |
| publisher | Wiley |
| record_format | marc |
| series2 | Wiley series in probability and statistics |
| spelling | Kulkarni, Sanjeev Verfasser aut An elementary introduction to statistical learning theory Sanjeev Kulkarni, Gilbert Harman Hoboken, N.J. Wiley ©2011 1 Online-Ressource (1 volume) txt rdacontent c rdamedia cr rdacarrier Wiley series in probability and statistics Introduction: Classification, Learning, Features, and Applications -- Probability -- Probability Densities -- The Pattern Recognition Problem -- The Optimal Bayes Decision Rule -- Learning from Examples -- The Nearest Neighbor Rule -- Kernel Rules -- Neural Networks: Perceptrons -- Multilayer Networks -- PAC Learning -- VC Dimension -- Infinite VC Dimension -- The Function Estimation Problem -- Learning Function Estimation -- Simplicity -- Support Vector Machines -- Boosting "A joint endeavor from leading researchers in the fields of philosophy and electrical engineering An Introduction to Statistical Learning Theory provides a broad and accessible introduction to rapidly evolving field of statistical pattern recognition and statistical learning theory. Exploring topics that are not often covered in introductory level books on statistical learning theory, including PAC learning, VC dimension, and simplicity, the authors present upper-undergraduate and graduate levels with the basic theory behind contemporary machine learning and uniquely suggest it serves as an excellent framework for philosophical thinking about inductive inference"--Back cover Includes bibliographical references and index Artificial Intelligence Pattern Recognition, Automated Statistics as Topic Aprenentatge automàtic / Mètodes estadístics Reconeixement de formes (Informàtica) COMPUTERS / Enterprise Applications / Business Intelligence Tools bisacsh COMPUTERS / Intelligence (AI) & Semantics bisacsh Maschinelles Lernen swd Statistik swd Machine learning / Statistical methods fast Pattern recognition systems fast Machine learning Statistical methods Pattern recognition systems Statistik (DE-588)4056995-0 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 s Statistik (DE-588)4056995-0 s 1\p DE-604 Harman, Gilbert 1938-2021 Sonstige (DE-588)109851439 oth Wiley InterScience (Online service) Sonstige oth http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=391376 Aggregator Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Kulkarni, Sanjeev An elementary introduction to statistical learning theory Artificial Intelligence Pattern Recognition, Automated Statistics as Topic Aprenentatge automàtic / Mètodes estadístics Reconeixement de formes (Informàtica) COMPUTERS / Enterprise Applications / Business Intelligence Tools bisacsh COMPUTERS / Intelligence (AI) & Semantics bisacsh Maschinelles Lernen swd Statistik swd Machine learning / Statistical methods fast Pattern recognition systems fast Machine learning Statistical methods Pattern recognition systems Statistik (DE-588)4056995-0 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)4056995-0 (DE-588)4193754-5 |
| title | An elementary introduction to statistical learning theory |
| title_auth | An elementary introduction to statistical learning theory |
| title_exact_search | An elementary introduction to statistical learning theory |
| title_full | An elementary introduction to statistical learning theory Sanjeev Kulkarni, Gilbert Harman |
| title_fullStr | An elementary introduction to statistical learning theory Sanjeev Kulkarni, Gilbert Harman |
| title_full_unstemmed | An elementary introduction to statistical learning theory Sanjeev Kulkarni, Gilbert Harman |
| title_short | An elementary introduction to statistical learning theory |
| title_sort | an elementary introduction to statistical learning theory |
| topic | Artificial Intelligence Pattern Recognition, Automated Statistics as Topic Aprenentatge automàtic / Mètodes estadístics Reconeixement de formes (Informàtica) COMPUTERS / Enterprise Applications / Business Intelligence Tools bisacsh COMPUTERS / Intelligence (AI) & Semantics bisacsh Maschinelles Lernen swd Statistik swd Machine learning / Statistical methods fast Pattern recognition systems fast Machine learning Statistical methods Pattern recognition systems Statistik (DE-588)4056995-0 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Artificial Intelligence Pattern Recognition, Automated Statistics as Topic Aprenentatge automàtic / Mètodes estadístics Reconeixement de formes (Informàtica) COMPUTERS / Enterprise Applications / Business Intelligence Tools COMPUTERS / Intelligence (AI) & Semantics Maschinelles Lernen Statistik Machine learning / Statistical methods Pattern recognition systems Machine learning Statistical methods |
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