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LEADER 00000nam a22006491i 4500 
003    MOCL 
005    20090709121008.0 
006    m    eo  d         
007    cr cn |||m|||a 
008    090708t20092009caua   foab   001 0 eng d 
019    200906AIM006 
020    9781598295481|q(electronic bk.) 
020    |z9781598295474|q(paperback) 
024 7  10.2200/S00196ED1V01Y200906AIM006|2doi 
035    (OCoLC)428541480 
035    (CaBNVSL)gtp00534961 
035    .b54108883 
040    CaBNVSL|beng|cCaBNVSL|dCaBNVSL 
050  4 Q325.75|b.Z485 2009 
082 04 006.31|222 
100 1  Zhu, Xiaojin.|0http://id.loc.gov/authorities/names/
       nr94017596 
245 10 Introduction to semi-supervised learning /|cXiaojin Zhu 
       and Andrew B. Goldberg. 
264  1 San Rafael, Calif. (1537 Fourth Street, San Rafael, CA  
       94901 USA) :|bMorgan & Claypool Publishers,|c[2009] 
264  4 |c©2009 
300    1 electronic text (xi, 116 pages :|billustrations) :
       |bdigital file. 
336    text|btxt|2rdacontent 
337    computer|bc|2rdamedia 
338    online resource|bcr|2rdacarrier 
340    |gpolychrome|2rdacc|0http://rdaregistry.info/termList/
       RDAColourContent/1003 
347    text file|2rdaft|0http://rdaregistry.info/termList/
       fileType/1002 
490 1  Synthesis lectures on artificial intelligence and machine 
       learning,|x1939-4616 ;|v# 6. 
500    Part of: Synthesis digital library of engineering and 
       computer science. 
500    Series from website. 
504    Includes bibliographical references (pages 95-112) and 
       index. 
505 0  Introduction to statistical machine learning -- The data -
       - Unsupervised learning -- Supervised learning -- Overview
       of semi-supervised learning -- Learning from both labeled 
       and unlabeled data -- How is semi-supervised learning 
       possible -- Inductive vs. transductive semi-supervised 
       learning -- Caveats -- Self-training models -- Mixture 
       models and EM -- Mixture models for supervised 
       classification -- Mixture models for semi-supervised 
       classification -- Optimization with the EM algorithm -- 
       The assumptions of mixture models -- Other issues in 
       generative models -- Cluster-then-label methods -- Co-
       training -- Two views of an instance -- Co-training -- The
       assumptions of co-training -- Multiview learning -- Graph-
       based semi-supervised learning -- Unlabeled data as 
       stepping stones -- The graph -- Mincut -- Harmonic 
       function -- Manifold regularization -- The assumption of 
       graph-based methods -- Semi-supervised support vector 
       machines -- Support vector machines -- Semi-supervised 
       support vector machines -- Entropy regularization -- The 
       assumption of S3VMS and entropy regularization -- Human 
       semi-supervised learning -- From machine learning to 
       cognitive science -- Study one: humans learn from 
       unlabeled test data -- Study two: presence of human semi-
       supervised learning in a simple task -- Study three: 
       absence of human semi-supervised learning in a complex 
       task -- Discussions -- Theory and outlook -- A simple PAC 
       bound for supervised learning -- A simple PAC bound for 
       semi-supervised learning -- Future directions of semi-
       supervised learning -- Basic mathematical reference -- 
       Semi-supervised learning software -- Symbols -- Biography.
506 1  Abstract freely available; full-text restricted to 
       subscribers or individual document purchasers. 
510 0  Compendex. 
510 0  INSPEC. 
510 0  Google scholar. 
510 0  Google book search. 
520 3  Semi-supervised learning is a learning paradigm concerned 
       with the study of how computers and natural systems such 
       as humans learn in the presence of both labeled and 
       unlabeled data. Traditionally, learning has been studied 
       either in the unsupervised paradigm (e.g., clustering, 
       outlier detection) where all the data is unlabeled, or in 
       the supervised paradigm (e.g., classification, regression)
       where all the data is labeled. The goal of semi-supervised
       learning is to understand how combining labeled and 
       unlabeled data may change the learning behavior, and 
       design algorithms that take advantage of such a 
       combination. Semi-supervised learning is of great interest
       in machine learning and data mining because it can use 
       readily available unlabeled data to improve supervised 
       learning tasks when the labeled data is scarce or 
       expensive. Semi-supervised learning also shows potential 
       as a quantitative tool to understand human category 
       learning, where most of the input is self-evidently 
       unlabeled. In this introductory book, we present some 
       popular semi-supervised learning models, including self-
       training, mixture models, co-training and multiview 
       learning, graph-based methods, and semisupervised support 
       vector machines. For each model, we discuss its basic 
       mathematical formulation. The success of semi-supervised 
       learning depends critically on some underlying 
       assumptions. We emphasize the assumptions made by each 
       model and give counterexamples when appropriate to 
       demonstrate the limitations of the different models. In 
       addition, we discuss semi-supervised learning for 
       cognitive psychology. Finally, we give a computational 
       learning theoretic perspective on semisupervised learning,
       and we conclude the book with a brief discussion of open 
       questions in the field. 
530    Also available in print. 
538    Mode of access: World Wide Web. 
538    System requirements: Adobe Acrobat reader. 
588    Title from PDF t.p. (viewed on July 8, 2009). 
650  0 Supervised learning (Machine learning)|0http://id.loc.gov/
       authorities/subjects/sh94008290 
650  0 Support vector machines.|0http://id.loc.gov/authorities/
       subjects/sh2008009003 
700 1  Goldberg, A. B.|q(Andrew B.)|0http://id.loc.gov/
       authorities/names/no2004048973 
730 0  Synthesis digital library of engineering and computer 
       science.|0http://id.loc.gov/authorities/names/n2016188085 
830  0 Synthesis lectures on artificial intelligence and machine 
       learning.|0http://id.loc.gov/authorities/names/
       no2008023636|x1939-4616 ;|v# 6. 
856 42 |3Abstract with links to full text|uhttps://
       ezp.lib.unimelb.edu.au/login?url=http://dx.doi.org/10.2200
       /S00196ED1V01Y200906AIM006|zConnect to ebook (University 
       of Melbourne only) 
907    .b54108883 
990    MARCIVE MELB 201906 
990    Synthesis Digital Library, Coll. 1-6 
990    Batch Ebook load (bud2) - do not edit, delete or attach 
       any records. 
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