LEADER 00000nam a22006491i 4500
006 m eo d
007 cr cn |||m|||a
008 090708t20092009caua foab 001 0 eng d
020 9781598295481|q(electronic bk.)
024 7 10.2200/S00196ED1V01Y200906AIM006|2doi
050 4 Q325.75|b.Z485 2009
082 04 006.31|222
100 1 Zhu, Xiaojin.|0http://id.loc.gov/authorities/names/
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
264 4 |c©2009
300 1 electronic text (xi, 116 pages :|billustrations) :
338 online resource|bcr|2rdacarrier
347 text file|2rdaft|0http://rdaregistry.info/termList/
490 1 Synthesis lectures on artificial intelligence and machine
learning,|x1939-4616 ;|v# 6.
500 Part of: Synthesis digital library of engineering and
500 Series from website.
504 Includes bibliographical references (pages 95-112) and
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/
650 0 Support vector machines.|0http://id.loc.gov/authorities/
700 1 Goldberg, A. B.|q(Andrew B.)|0http://id.loc.gov/
730 0 Synthesis digital library of engineering and computer
830 0 Synthesis lectures on artificial intelligence and machine
no2008023636|x1939-4616 ;|v# 6.
856 42 |3Abstract with links to full text|uhttps://
/S00196ED1V01Y200906AIM006|zConnect to ebook (University
of Melbourne only)
990 MARCIVE MELB 201906
990 Synthesis Digital Library, Coll. 1-6
990 Batch Ebook load (bud2) - do not edit, delete or attach
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