東華大學圖書館 |

Active learning

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Active learning/ Burr Settles.
Author:	Settles, Burr.
Published:	San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA) :Morgan & Claypool, : c2012.,
Description:	1 online resource (xiii, 100 p.) :ill.
Notes:	Part of: Synthesis digital library of engineering and computer science.
[NT 15003449]:	Preface -- Acknowledgments --
[NT 15003449]:	1. Automating inquiry -- 1.1 A thought experiment -- 1.2 Active learning -- 1.3 Scenarios for active learning --
[NT 15003449]:	2. Uncertainty sampling -- 2.1 Pushing the boundaries -- 2.2 An example -- 2.3 Measures of uncertainty -- 2.4 Beyond classification -- 2.5 Discussion --
[NT 15003449]:	3. Searching through the hypothesis space -- 3.1 The version space -- 3.2 Uncertainty sampling as version space search -- 3.3 Query by disagreement -- 3.4 Query by committee -- 3.5 Discussion --
[NT 15003449]:	4. Minimizing expected error and variance -- 4.1 Expected error reduction -- 4.2 Variance reduction -- 4.3 Batch queries and submodularity -- 4.4 Discussion --
[NT 15003449]:	5. Exploiting structure in data -- 5.1 Density-weighted methods -- 5.2 Cluster-based active learning -- 5.3 Active + semi-supervised learning -- 5.4 Discussion --
[NT 15003449]:	6. Theory -- 6.1 A unified view -- 6.2 A PAC bound for active learning -- 6.3 Discussion --
[NT 15003449]:	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 --
[NT 15003449]:	A. Nomenclature reference -- Bibliography -- Author's biography -- Index.
Subject:	Supervised learning (Machine learning) -
Online resource:	http://dx.doi.org/10.2200/S00429ED1V01Y201207AIM018
Online resource:	http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=467776
ISBN:	9781608457267 (electronic bk.)

Active learning
Settles, Burr.

Active learning[electronic resource] /Burr Settles. - San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA) :Morgan & Claypool,c2012. - 1 online resource (xiii, 100 p.) :ill. - Synthesis lectures on artificial intelligence and machine learning,#181939-4616 ;. - Synthesis lectures on artificial intelligence and machine learning ;#18..

Part of: Synthesis digital library of engineering and computer science.

Includes bibliographical references (p. 81-96) and index.

Preface -- Acknowledgments --

Compendex

The key idea behind active learning is that a machine learning algorithm can perform better with less training if it is allowed to choose the data from which it learns. An active learner may pose "queries," usually in the form of unlabeled data instances to be labeled by an "oracle" (e.g., a human annotator) that already understands the nature of the problem. This sort of approach is well-motivated in many modern machine learning and data mining applications, where unlabeled data may be abundant or easy to come by, but training labels are difficult, time-consuming, or expensive to obtain. This book is a general introduction to active learning. It outlines several scenarios in which queries might be formulated, and details many query selection algorithms which have been organized into four broad categories, or "query selection frameworks."We also touch on some of the theoretical foundations of active learning, and conclude with an overview of the strengths and weaknesses of these approaches in practice, including a summary of ongoing work to address these open challenges and opportunities.

ISBN: 9781608457267 (electronic bk.)

Standard No.: 10.2200/S00429ED1V01Y201207AIM018doiSubjects--Topical Terms:

731226
Supervised learning (Machine learning)
Subjects--Index Terms:

active learningIndex Terms--Genre/Form:

542853
Electronic books.

LC Class. No.: Q325.75 / .S472 2012

Dewey Class. No.: 006.31

Active learning
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300 $a 1 online resource (xiii, 100 p.) : $b ill.
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500 $a Title from PDF t.p. (Morgan & Claypool, viewed on July 14, 2012).
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505 0 $a Preface -- Acknowledgments --
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505 8 $a 2. Uncertainty sampling -- 2.1 Pushing the boundaries -- 2.2 An example -- 2.3 Measures of uncertainty -- 2.4 Beyond classification -- 2.5 Discussion --
505 8 $a 3. Searching through the hypothesis space -- 3.1 The version space -- 3.2 Uncertainty sampling as version space search -- 3.3 Query by disagreement -- 3.4 Query by committee -- 3.5 Discussion --
505 8 $a 4. Minimizing expected error and variance -- 4.1 Expected error reduction -- 4.2 Variance reduction -- 4.3 Batch queries and submodularity -- 4.4 Discussion --
505 8 $a 5. Exploiting structure in data -- 5.1 Density-weighted methods -- 5.2 Cluster-based active learning -- 5.3 Active + semi-supervised learning -- 5.4 Discussion --
505 8 $a 6. Theory -- 6.1 A unified view -- 6.2 A PAC bound for active learning -- 6.3 Discussion --
505 8 $a 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 --
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