Semi-supervised learning /:
A comprehensive review of an area of machine learning that deals with the use of unlabeled data in classification problems, this text looks at state-of-the-art algorithms, applications benchmark experiments, and directions for future research.
Gespeichert in:
| Weitere Verfasser: | , , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Cambridge, Mass. :
MIT Press,
©2006.
|
| Schriftenreihe: | Adaptive computation and machine learning.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | A comprehensive review of an area of machine learning that deals with the use of unlabeled data in classification problems, this text looks at state-of-the-art algorithms, applications benchmark experiments, and directions for future research. |
| Beschreibung: | 1 online resource (x, 508 pages) : illustrations |
| Bibliographie: | Includes bibliographical references (pages 479-497). |
| ISBN: | 9780262255899 0262255898 0262033585 9780262033589 1282096184 9781282096189 1429414081 9781429414081 |
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| 504 | |a Includes bibliographical references (pages 479-497). | ||
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| 520 | 8 | |a A comprehensive review of an area of machine learning that deals with the use of unlabeled data in classification problems, this text looks at state-of-the-art algorithms, applications benchmark experiments, and directions for future research. | |
| 505 | 0 | |a Series Foreword; Preface; 1 -- Introduction to Semi-Supervised Learning; 2 -- A Taxonomy for Semi-Supervised Learning Methods; 3 -- Semi-Supervised Text Classification Using EM; 4 -- Risks of Semi-Supervised Learning: How Unlabeled Data Can Degrade Performance of Generative Classifiers; 5 -- Probabilistic Semi-Supervised Clustering with Constraints; 6 -- Transductive Support Vector Machines; 7 -- Semi-Supervised Learning Using Semi- Definite Programming; 8 -- Gaussian Processes and the Null-Category Noise Model; 9 -- Entropy Regularization; 10 -- Data-Dependent Regularization. | |
| 505 | 8 | |a 11 -- Label Propagation and Quadratic Criterion12 -- The Geometric Basis of Semi-Supervised Learning; 13 -- Discrete Regularization; 14 -- Semi-Supervised Learning with Conditional Harmonic Mixing; 15 -- Graph Kernels by Spectral Transforms; 16- Spectral Methods for Dimensionality Reduction; 17 -- Modifying Distances; 18 -- Large-Scale Algorithms; 19 -- Semi-Supervised Protein Classification Using Cluster Kernels; 20 -- Prediction of Protein Function from Networks; 21 -- Analysis of Benchmarks; 22 -- An Augmented PAC Model for Semi- Supervised Learning. | |
| 505 | 8 | |a 23 -- Metric-Based Approaches for Semi- Supervised Regression and Classification24 -- Transductive Inference and Semi-Supervised Learning; 25 -- A Discussion of Semi-Supervised Learning and Transduction; References; Notation and Symbols; Contributors; Index. | |
| 546 | |a English. | ||
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| 700 | 1 | |a Schölkopf, Bernhard. | |
| 700 | 1 | |a Zien, Alexander. | |
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Datensatz im Suchindex
| DE-BY-FWS_katkey | ZDB-4-EBA-ocm76824411 |
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| _version_ | 1816881642839474176 |
| adam_text | |
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| author2 | Chapelle, Olivier Schölkopf, Bernhard Zien, Alexander |
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| author_facet | Chapelle, Olivier Schölkopf, Bernhard Zien, Alexander |
| author_sort | Chapelle, Olivier |
| building | Verbundindex |
| bvnumber | localFWS |
| callnumber-first | Q - Science |
| callnumber-label | Q325 |
| callnumber-raw | Q325.75 .S42 2006eb |
| callnumber-search | Q325.75 .S42 2006eb |
| callnumber-sort | Q 3325.75 S42 42006EB |
| callnumber-subject | Q - General Science |
| collection | ZDB-4-EBA |
| contents | Series Foreword; Preface; 1 -- Introduction to Semi-Supervised Learning; 2 -- A Taxonomy for Semi-Supervised Learning Methods; 3 -- Semi-Supervised Text Classification Using EM; 4 -- Risks of Semi-Supervised Learning: How Unlabeled Data Can Degrade Performance of Generative Classifiers; 5 -- Probabilistic Semi-Supervised Clustering with Constraints; 6 -- Transductive Support Vector Machines; 7 -- Semi-Supervised Learning Using Semi- Definite Programming; 8 -- Gaussian Processes and the Null-Category Noise Model; 9 -- Entropy Regularization; 10 -- Data-Dependent Regularization. 11 -- Label Propagation and Quadratic Criterion12 -- The Geometric Basis of Semi-Supervised Learning; 13 -- Discrete Regularization; 14 -- Semi-Supervised Learning with Conditional Harmonic Mixing; 15 -- Graph Kernels by Spectral Transforms; 16- Spectral Methods for Dimensionality Reduction; 17 -- Modifying Distances; 18 -- Large-Scale Algorithms; 19 -- Semi-Supervised Protein Classification Using Cluster Kernels; 20 -- Prediction of Protein Function from Networks; 21 -- Analysis of Benchmarks; 22 -- An Augmented PAC Model for Semi- Supervised Learning. 23 -- Metric-Based Approaches for Semi- Supervised Regression and Classification24 -- Transductive Inference and Semi-Supervised Learning; 25 -- A Discussion of Semi-Supervised Learning and Transduction; References; Notation and Symbols; Contributors; Index. |
| ctrlnum | (OCoLC)76824411 |
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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-EBA-ocm76824411 |
| illustrated | Illustrated |
| indexdate | 2024-11-27T13:15:58Z |
| institution | BVB |
| isbn | 9780262255899 0262255898 0262033585 9780262033589 1282096184 9781282096189 1429414081 9781429414081 |
| language | English |
| oclc_num | 76824411 |
| open_access_boolean | |
| owner | MAIN DE-863 DE-BY-FWS |
| owner_facet | MAIN DE-863 DE-BY-FWS |
| physical | 1 online resource (x, 508 pages) : illustrations |
| psigel | ZDB-4-EBA |
| publishDate | 2006 |
| publishDateSearch | 2006 |
| publishDateSort | 2006 |
| publisher | MIT Press, |
| record_format | marc |
| series | Adaptive computation and machine learning. |
| series2 | Adaptive computation and machine learning |
| spelling | Semi-supervised learning / [edited by] Olivier Chapelle, Bernhard Schölkopf, Alexander Zien. Cambridge, Mass. : MIT Press, ©2006. 1 online resource (x, 508 pages) : illustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier data file rda Adaptive computation and machine learning Includes bibliographical references (pages 479-497). Print version record. A comprehensive review of an area of machine learning that deals with the use of unlabeled data in classification problems, this text looks at state-of-the-art algorithms, applications benchmark experiments, and directions for future research. Series Foreword; Preface; 1 -- Introduction to Semi-Supervised Learning; 2 -- A Taxonomy for Semi-Supervised Learning Methods; 3 -- Semi-Supervised Text Classification Using EM; 4 -- Risks of Semi-Supervised Learning: How Unlabeled Data Can Degrade Performance of Generative Classifiers; 5 -- Probabilistic Semi-Supervised Clustering with Constraints; 6 -- Transductive Support Vector Machines; 7 -- Semi-Supervised Learning Using Semi- Definite Programming; 8 -- Gaussian Processes and the Null-Category Noise Model; 9 -- Entropy Regularization; 10 -- Data-Dependent Regularization. 11 -- Label Propagation and Quadratic Criterion12 -- The Geometric Basis of Semi-Supervised Learning; 13 -- Discrete Regularization; 14 -- Semi-Supervised Learning with Conditional Harmonic Mixing; 15 -- Graph Kernels by Spectral Transforms; 16- Spectral Methods for Dimensionality Reduction; 17 -- Modifying Distances; 18 -- Large-Scale Algorithms; 19 -- Semi-Supervised Protein Classification Using Cluster Kernels; 20 -- Prediction of Protein Function from Networks; 21 -- Analysis of Benchmarks; 22 -- An Augmented PAC Model for Semi- Supervised Learning. 23 -- Metric-Based Approaches for Semi- Supervised Regression and Classification24 -- Transductive Inference and Semi-Supervised Learning; 25 -- A Discussion of Semi-Supervised Learning and Transduction; References; Notation and Symbols; Contributors; Index. English. Supervised learning (Machine learning) http://id.loc.gov/authorities/subjects/sh94008290 Apprentissage supervisé (Intelligence artificielle) COMPUTERS Enterprise Applications Business Intelligence Tools. bisacsh COMPUTERS Intelligence (AI) & Semantics. bisacsh Supervised learning (Machine learning) fast COMPUTER SCIENCE/Machine Learning & Neural Networks Chapelle, Olivier. Schölkopf, Bernhard. Zien, Alexander. has work: Semi-supervised learning (Text) https://id.oclc.org/worldcat/entity/E39PCGFQqvqhPRRX4gg9XkPrtq https://id.oclc.org/worldcat/ontology/hasWork Print version: Semi-supervised learning. Cambridge, Mass. : MIT Press, ©2006 0262033585 (DLC) 2006044448 (OCoLC)64898359 Adaptive computation and machine learning. http://id.loc.gov/authorities/names/n97066095 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=170037 Volltext |
| spellingShingle | Semi-supervised learning / Adaptive computation and machine learning. Series Foreword; Preface; 1 -- Introduction to Semi-Supervised Learning; 2 -- A Taxonomy for Semi-Supervised Learning Methods; 3 -- Semi-Supervised Text Classification Using EM; 4 -- Risks of Semi-Supervised Learning: How Unlabeled Data Can Degrade Performance of Generative Classifiers; 5 -- Probabilistic Semi-Supervised Clustering with Constraints; 6 -- Transductive Support Vector Machines; 7 -- Semi-Supervised Learning Using Semi- Definite Programming; 8 -- Gaussian Processes and the Null-Category Noise Model; 9 -- Entropy Regularization; 10 -- Data-Dependent Regularization. 11 -- Label Propagation and Quadratic Criterion12 -- The Geometric Basis of Semi-Supervised Learning; 13 -- Discrete Regularization; 14 -- Semi-Supervised Learning with Conditional Harmonic Mixing; 15 -- Graph Kernels by Spectral Transforms; 16- Spectral Methods for Dimensionality Reduction; 17 -- Modifying Distances; 18 -- Large-Scale Algorithms; 19 -- Semi-Supervised Protein Classification Using Cluster Kernels; 20 -- Prediction of Protein Function from Networks; 21 -- Analysis of Benchmarks; 22 -- An Augmented PAC Model for Semi- Supervised Learning. 23 -- Metric-Based Approaches for Semi- Supervised Regression and Classification24 -- Transductive Inference and Semi-Supervised Learning; 25 -- A Discussion of Semi-Supervised Learning and Transduction; References; Notation and Symbols; Contributors; Index. Supervised learning (Machine learning) http://id.loc.gov/authorities/subjects/sh94008290 Apprentissage supervisé (Intelligence artificielle) COMPUTERS Enterprise Applications Business Intelligence Tools. bisacsh COMPUTERS Intelligence (AI) & Semantics. bisacsh Supervised learning (Machine learning) fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh94008290 |
| title | Semi-supervised learning / |
| title_auth | Semi-supervised learning / |
| title_exact_search | Semi-supervised learning / |
| title_full | Semi-supervised learning / [edited by] Olivier Chapelle, Bernhard Schölkopf, Alexander Zien. |
| title_fullStr | Semi-supervised learning / [edited by] Olivier Chapelle, Bernhard Schölkopf, Alexander Zien. |
| title_full_unstemmed | Semi-supervised learning / [edited by] Olivier Chapelle, Bernhard Schölkopf, Alexander Zien. |
| title_short | Semi-supervised learning / |
| title_sort | semi supervised learning |
| topic | Supervised learning (Machine learning) http://id.loc.gov/authorities/subjects/sh94008290 Apprentissage supervisé (Intelligence artificielle) COMPUTERS Enterprise Applications Business Intelligence Tools. bisacsh COMPUTERS Intelligence (AI) & Semantics. bisacsh Supervised learning (Machine learning) fast |
| topic_facet | Supervised learning (Machine learning) Apprentissage supervisé (Intelligence artificielle) COMPUTERS Enterprise Applications Business Intelligence Tools. COMPUTERS Intelligence (AI) & Semantics. |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=170037 |
| work_keys_str_mv | AT chapelleolivier semisupervisedlearning AT scholkopfbernhard semisupervisedlearning AT zienalexander semisupervisedlearning |