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The design and analysis of efficient learning algorithms

Autor: Robert E Schapire
Editorial: Cambridge, Mass. : MIT Press, ©1992.
Serie: ACM doctoral dissertation award, 1991.
Edición/Formato:   Libro : Inglés (eng)Ver todas las ediciones y todos los formatos
Base de datos:WorldCat
Resumen:

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  Leer más

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Formato físico adicional: Online version:
Schapire, Robert E.
Design and analysis of efficient learning algorithms.
Cambridge, Mass. : MIT Press, c1992
(OCoLC)654250685
Tipo de documento: Libro/Texto
Todos autores / colaboradores: Robert E Schapire
ISBN: 0262193256 9780262193252
Número OCLC: 26307663
Descripción: ix, 217 p. : ill. ; 24 cm.
Contenido: 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.
Título de la serie: ACM doctoral dissertation award, 1991.
Responsabilidad: Robert E. Schapire.

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