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The design and analysis of efficient learning algorithms

저자: Robert E Schapire
출판사: Cambridge, Mass. : MIT Press, ©1992.
시리즈: ACM doctoral dissertation award, 1991.
판/형식:   도서 : 영어모든 판과 형식 보기
데이터베이스:WorldCat
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This text describes results derived from the mathematically oriented framework of computational learning theory. Focusing on the design of efficient learning algorithms and their performance, it  더 읽기…

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추가적인 물리적 형식: Online version:
Schapire, Robert E.
Design and analysis of efficient learning algorithms.
Cambridge, Mass. : MIT Press, c1992
(OCoLC)654250685
문서 형식:
모든 저자 / 참여자: Robert E Schapire
ISBN: 0262193256 9780262193252
OCLC 번호: 26307663
설명: ix, 217 p. : ill. ; 24 cm.
내용: Part 1 The strength of weak learnability: the equivalence of strong and weak learnability; improving Learn's time and sample complexity; variations on the learning model; general complexity bounds for PAC learning; conclusions and open problems. Part 2 Statistical methods for inference of read-once formulas: exact identification of read-once majority formulas; exact identification of read-once positive NAND formulas; handling random misclassification noise; learning unbounded-depth formulas; learning probabilistic read-once formulas; conclusion and open problems. Part 3 Efficient distribution-free learning of probabilistic concepts: the learning model; efficient algorithms - the direct approach; hypothesis testing and expected loss; uniform convergence methods; a lower bound on sample size; Occam's Razor for genenral loss functions; conclusions and open problems. Part 4 Inference of finite automata using homing sequences: two representations of finite automata; homing sequences; a state-based algorithm for general automata; a diversity-based algorithm for general automata; a state-based algorithm for permutation automata; a diversity-based algorithm for permulation automata; experimental results; conclusions and open questions.
일련 제목: ACM doctoral dissertation award, 1991.
책임: Robert E. Schapire.

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