Verfügbarkeit: Active learning

Active learning:

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Bibliographische Detailangaben
1. Verfasser:	Settles, Burr (VerfasserIn)
Format:	Buch
Sprache:	English
Veröffentlicht:	San Rafael, Cal. Morgan & Claypool Publishers [2012]
Schriftenreihe:	Synthesis lectures on artificial intelligence and machine learning 18
Schlagworte:	Künstliche Intelligenz Maschinelles Lernen
Online-Zugang:	Inhaltsverzeichnis
Beschreibung:	XIII, 100 Seiten Illustrationen
ISBN:	9781608457250

Internformat

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Datensatz im Suchindex

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adam_text	IX Contents Preface.....................................................................xi Acknowledgments...........................................................xiii 1 Automating Inquiry............................................................1 1.1 A Thought Experiment.................................................. 1 1.2 Active Learning....................................................... 3 1.3 Scenarios for Active Learning......................................... 5 2 Uncertainty Sampling........................................................ 11 2.1 Pushing the Boundaries .............................................. 11 2.2 An Example........................................................... 12 2.3 Measures of Uncertainty.............................................. 13 2.4 Beyond Classification ............................................... 16 2.5 Discussion........................................................... 18 3 SearchingThrough the Hypothesis Space....................................... 21 3.1 The Version Space.....................................................21 3.2 Uncertainty Sampling as Version Space Search..........................22 3.3 Query by Disagreement ................................................24 3.4 Query by Committee....................................................28 3.5 Discussion............................................................32 4 Minimizing Expected Error and Variance ..................................... 37 4.1 Expected Error Reduction..............................................37 4.2 Variance Reduction....................................................40 4.3 Batch Queries and Submodularity.......................................44 4.4 Discussion............................................................46 5 Exploiting Structure in Data................................................ 47 5.1 Density-Weighted Methods..............................................47 5.2 Cluster-Based Active Learning.........................................49 5.3 Active + Semi-Supervised Learning.... 5.4 Discussion........................... 6 Theory...................................... 6.1 A Unified View....................... 6.2 A PAC Bound for Active Learning...... 6.3 Discussion........................... 7 Practical Considerations.................... 7.1 Which Algorithm is Best? ............ 7.2 Real Labeling Costs.................. 7.3 Alternative Query Types.............. 7.4 Skewed Label Distributions........... 7.5 Unreliable Oracles................... 7.6 Multi-Task Active Learning........... 7.7 Data Reuse and the Unknown Model Class 7.8 Stopping Criteria.................... A Nomenclature Reference...................... Bibliography............................... Author’s Biography......................... 53 54 55 55 57 61 63 63 65 68 72 73 74 76 77 79 81 97 Index 99
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title	Active learning
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