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Introduction to semi-supervised learning (Record no. 561690)

000 -LEADER
fixed length control field 06141nam a2200697 i 4500
001 - CONTROL NUMBER
control field 6813505
003 - CONTROL NUMBER IDENTIFIER
control field IEEE
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20200413152854.0
006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS
fixed length control field m eo d
007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION
fixed length control field cr cn |||m|||a
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 090708s2009 caua foab 001 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781598295481 (electronic bk.)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
Canceled/invalid ISBN 9781598295474 (pbk.)
024 7# - OTHER STANDARD IDENTIFIER
Standard number or code 10.2200/S00196ED1V01Y200906AIM006
Source of number or code doi
035 ## - SYSTEM CONTROL NUMBER
System control number (CaBNVSL)gtp00534961
035 ## - SYSTEM CONTROL NUMBER
System control number (OCoLC)428541480
040 ## - CATALOGING SOURCE
Original cataloging agency CaBNVSL
Transcribing agency CaBNVSL
Modifying agency CaBNVSL
050 #4 - LIBRARY OF CONGRESS CALL NUMBER
Classification number Q325.75
Item number .Z485 2009
082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 006.31
Edition number 22
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Zhu, Xiaojin.
245 10 - TITLE STATEMENT
Title Introduction to semi-supervised learning
Medium [electronic resource] /
Statement of responsibility, etc. Xiaojin Zhu and Andrew B. Goldberg.
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Place of publication, distribution, etc. San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA) :
Name of publisher, distributor, etc. Morgan & Claypool Publishers,
Date of publication, distribution, etc. c2009.
300 ## - PHYSICAL DESCRIPTION
Extent 1 electronic text (xi, 116 p. : ill.) :
Other physical details digital file.
490 1# - SERIES STATEMENT
Series statement Synthesis lectures on artificial intelligence and machine learning,
International Standard Serial Number 1939-4616 ;
Volume/sequential designation # 6
538 ## - SYSTEM DETAILS NOTE
System details note Mode of access: World Wide Web.
538 ## - SYSTEM DETAILS NOTE
System details note System requirements: Adobe Acrobat reader.
500 ## - GENERAL NOTE
General note Part of: Synthesis digital library of engineering and computer science.
500 ## - GENERAL NOTE
General note Series from website.
504 ## - BIBLIOGRAPHY, ETC. NOTE
Bibliography, etc. note Includes bibliographical references (p. 95-112) and index.
505 0# - FORMATTED CONTENTS NOTE
Formatted contents note 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# - RESTRICTIONS ON ACCESS NOTE
Terms governing access Abstract freely available; full-text restricted to subscribers or individual document purchasers.
510 0# - CITATION/REFERENCES NOTE
Name of source Compendex
510 0# - CITATION/REFERENCES NOTE
Name of source INSPEC
510 0# - CITATION/REFERENCES NOTE
Name of source Google scholar
510 0# - CITATION/REFERENCES NOTE
Name of source Google book search
520 3# - SUMMARY, ETC.
Summary, etc. 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 ## - ADDITIONAL PHYSICAL FORM AVAILABLE NOTE
Additional physical form available note Also available in print.
588 ## - SOURCE OF DESCRIPTION NOTE
Source of description note Title from PDF t.p. (viewed on July 8, 2009).
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Supervised learning (Machine learning)
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Support vector machines.
690 ## - LOCAL SUBJECT ADDED ENTRY--TOPICAL TERM (OCLC, RLIN)
Topical term or geographic name as entry element Semi-supervised learning
690 ## - LOCAL SUBJECT ADDED ENTRY--TOPICAL TERM (OCLC, RLIN)
Topical term or geographic name as entry element Transductive learning
690 ## - LOCAL SUBJECT ADDED ENTRY--TOPICAL TERM (OCLC, RLIN)
Topical term or geographic name as entry element Self-training
690 ## - LOCAL SUBJECT ADDED ENTRY--TOPICAL TERM (OCLC, RLIN)
Topical term or geographic name as entry element Gaussian mixture model
690 ## - LOCAL SUBJECT ADDED ENTRY--TOPICAL TERM (OCLC, RLIN)
Topical term or geographic name as entry element Expectation maximization (EM)
690 ## - LOCAL SUBJECT ADDED ENTRY--TOPICAL TERM (OCLC, RLIN)
Topical term or geographic name as entry element Cluster-then-label
690 ## - LOCAL SUBJECT ADDED ENTRY--TOPICAL TERM (OCLC, RLIN)
Topical term or geographic name as entry element Co-training
690 ## - LOCAL SUBJECT ADDED ENTRY--TOPICAL TERM (OCLC, RLIN)
Topical term or geographic name as entry element Multiview learning
690 ## - LOCAL SUBJECT ADDED ENTRY--TOPICAL TERM (OCLC, RLIN)
Topical term or geographic name as entry element Mincut
690 ## - LOCAL SUBJECT ADDED ENTRY--TOPICAL TERM (OCLC, RLIN)
Topical term or geographic name as entry element Harmonic function
690 ## - LOCAL SUBJECT ADDED ENTRY--TOPICAL TERM (OCLC, RLIN)
Topical term or geographic name as entry element Label propagation
690 ## - LOCAL SUBJECT ADDED ENTRY--TOPICAL TERM (OCLC, RLIN)
Topical term or geographic name as entry element Manifold regularization
690 ## - LOCAL SUBJECT ADDED ENTRY--TOPICAL TERM (OCLC, RLIN)
Topical term or geographic name as entry element Semi-supervised support vector machines (S3VM)
690 ## - LOCAL SUBJECT ADDED ENTRY--TOPICAL TERM (OCLC, RLIN)
Topical term or geographic name as entry element Transductive support vector machines (TSVM)
690 ## - LOCAL SUBJECT ADDED ENTRY--TOPICAL TERM (OCLC, RLIN)
Topical term or geographic name as entry element Entropy regularization
690 ## - LOCAL SUBJECT ADDED ENTRY--TOPICAL TERM (OCLC, RLIN)
Topical term or geographic name as entry element Human semi-supervised learning
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Goldberg, Andrew B.
730 0# - ADDED ENTRY--UNIFORM TITLE
Uniform title Synthesis digital library of engineering and computer science.
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE
Uniform title Synthesis lectures on artificial intelligence and machine learning,
International Standard Serial Number 1939-4616 ;
Volume/sequential designation # 6.
856 42 - ELECTRONIC LOCATION AND ACCESS
Materials specified Abstract with links to resource
Uniform Resource Identifier http://ieeexplore.ieee.org/servlet/opac?bknumber=6813505
Holdings
Withdrawn status Lost status Damaged status Not for loan Permanent Location Current Location Date acquired Barcode Date last seen Price effective from Koha item type
        PK Kelkar Library, IIT Kanpur PK Kelkar Library, IIT Kanpur 2020-04-13 EBKE190 2020-04-13 2020-04-13 E books

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