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Semisupervised learning in computational linguistics

Semisupervised learning in computational linguistics (1회 대출)

자료유형
단행본
개인저자
Abney, Steven P.
서명 / 저자사항
Semisupervised learning in computational linguistics / Steven Abney.
발행사항
Boca Raton, FL :   Chapman & Hall/CRC ,   c2008.  
형태사항
xi, 308 p. : ill. ; 25 cm.
총서사항
Computer science and data analysis series
ISBN
9781584885597 (alk. paper) 1584885599 (alk. paper)
서지주기
Includes bibliographical references (p. 277-299) and index.
일반주제명
Computational linguistics -- Study and teaching (Higher)
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001 000045524123
005 20090521115223
008 070604s2008 flua b 001 0 eng
010 ▼a 2007022858
020 ▼a 9781584885597 (alk. paper)
020 ▼a 1584885599 (alk. paper)
035 ▼a (KERIS)REF000013123687
040 ▼a DLC ▼c DLC ▼d BTCTA ▼d BAKER ▼d C#P ▼d YDXCP ▼d DLC ▼d 211009
050 0 0 ▼a P98.3 ▼b .A26 2008
082 0 0 ▼a 410.285 ▼2 22
090 ▼a 410.285 ▼b A153s
100 1 ▼a Abney, Steven P.
245 1 0 ▼a Semisupervised learning in computational linguistics / ▼c Steven Abney.
260 ▼a Boca Raton, FL : ▼b Chapman & Hall/CRC , ▼c c2008.
300 ▼a xi, 308 p. : ▼b ill. ; ▼c 25 cm.
490 1 ▼a Computer science and data analysis series
504 ▼a Includes bibliographical references (p. 277-299) and index.
650 0 ▼a Computational linguistics ▼x Study and teaching (Higher)
830 0 ▼a Series in computer science and data analysis.
945 ▼a KINS

소장정보

No. 소장처 청구기호 등록번호 도서상태 반납예정일 예약 서비스
No. 1 소장처 중앙도서관/서고6층/ 청구기호 410.285 A153s 등록번호 111537107 도서상태 대출가능 반납예정일 예약 서비스 B M

컨텐츠정보

목차

INTRODUCTIONA brief history Semisupervised learning Organization and assumptionsSELF-TRAINING AND CO-TRAINING Classification Self-training Co-trainingAPPLICATIONS OF SELF-TRAINING AND CO-TRAINING Part-of-speech tagging Information extraction Parsing Word sensesCLASSIFICATION Two simple classifiers Abstract setting Evaluating detectors and classifiers that abstain Binary classifiers and ECOCMATHEMATICS FOR BOUNDARY-ORIENTED METHODS Linear separators The gradient Constrained optimizationBOUNDARY-ORIENTED METHODS The perceptron Game self-teaching Boosting Support vector machines (SVMs)Null-category noise modelCLUSTERING Cluster and label Clustering conceptsHierarchical clustering Self-training revisited Graph mincut Label propagation Bibliographic notesGENERATIVE MODELSGaussian mixturesThe EM algorithmAGREEMENT CONSTRAINTS Co-trainingAgreement-based self-teaching Random fieldsBibliographic notesPROPAGATION METHODS Label propagation Random walks Harmonic functionsFluidsComputing the solution Graph mincuts revisited Bibliographic notesMATHEMATICS FOR SPECTRAL METHODS Some basic conceptsEigenvalues and eigenvectorsEigenvalues and the scaling effects of a matrixBibliographic notesSPECTRAL METHODSSimple harmonic motionSpectra of matrices and graphsSpectral clusteringSpectral methods for semisupervised learningBibliographic notesBIBLIOGRAPHY INDEX


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