000			06141nam a2200697 i 4500
001			6813505
003			IEEE
005			20200413152854.0
006			m eo d
007			cr cn |||m|||a
008			090708s2009 caua foab 001 0 eng d
020			_a9781598295481 (electronic bk.)
020			_z9781598295474 (pbk.)
024	7		_a10.2200/S00196ED1V01Y200906AIM006 _2doi
035			_a(CaBNVSL)gtp00534961
035			_a(OCoLC)428541480
040			_aCaBNVSL _cCaBNVSL _dCaBNVSL
050		4	_aQ325.75 _b.Z485 2009
082	0	4	_a006.31 _222
100	1		_aZhu, Xiaojin.
245	1	0	_aIntroduction to semi-supervised learning _h[electronic resource] / _cXiaojin Zhu and Andrew B. Goldberg.
260			_aSan Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA) : _bMorgan & Claypool Publishers, _cc2009.
300			_a1 electronic text (xi, 116 p. : ill.) : _bdigital file.
490	1		_aSynthesis lectures on artificial intelligence and machine learning, _x1939-4616 ; _v# 6
538			_aMode of access: World Wide Web.
538			_aSystem requirements: Adobe Acrobat reader.
500			_aPart of: Synthesis digital library of engineering and computer science.
500			_aSeries from website.
504			_aIncludes bibliographical references (p. 95-112) and index.
505	0		_aIntroduction 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		_aAbstract freely available; full-text restricted to subscribers or individual document purchasers.
510	0		_aCompendex
510	0		_aINSPEC
510	0		_aGoogle scholar
510	0		_aGoogle book search
520	3		_aSemi-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			_aAlso available in print.
588			_aTitle from PDF t.p. (viewed on July 8, 2009).
650		0	_aSupervised learning (Machine learning)
650		0	_aSupport vector machines.
690			_aSemi-supervised learning
690			_aTransductive learning
690			_aSelf-training
690			_aGaussian mixture model
690			_aExpectation maximization (EM)
690			_aCluster-then-label
690			_aCo-training
690			_aMultiview learning
690			_aMincut
690			_aHarmonic function
690			_aLabel propagation
690			_aManifold regularization
690			_aSemi-supervised support vector machines (S3VM)
690			_aTransductive support vector machines (TSVM)
690			_aEntropy regularization
690			_aHuman semi-supervised learning
700	1		_aGoldberg, Andrew B.
730	0		_aSynthesis digital library of engineering and computer science.
830		0	_aSynthesis lectures on artificial intelligence and machine learning, _x1939-4616 ; _v# 6.
856	4	2	_3Abstract with links to resource _uhttp://ieeexplore.ieee.org/servlet/opac?bknumber=6813505
999			_c561690 _d561690