Titlebook: Computational Learning Theory; 4th European Confere Paul Fischer,Hans Ulrich Simon Conference proceedings 1999 Springer-Verlag Berlin Heide

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https://doi.org/10.1007/3-540-49097-3Algorithmic Learning; Computational Learning; Inductive Inference; Online Learning; learning; learning th

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Theoretical Views of Boostingey theoretical work on boosting including analyses of AdaBoost’s training error and generalization error, connections between boosting and game theory, methods of estimating probabilities using boosting, and extensions of AdaBoost for multiclass classification problems. We also briefly mention some empirical work.

引起痛苦

Learning Multiplicity Automata from Smallest Counterexamples improves on an earlier result of Bergadano and Varricchio. A unique representation for MAs is introduced. Our algorithm learns this representation. We also show that any learning algorithm for MAs needs at least . smallest counterexamples. Thus our upper bound on the number of counterexamples cannot be improved substantially.

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產(chǎn)生

Lower Bounds on the Rate of Convergence of Nonparametric Pattern Recognitionch are arbitrarily close to Yang’s minimax lower bounds, if the a posteriori probability function is in the classes used by Stone and others. The rates equal to the ones on the corresponding regression estimation problem. Thus for these classes classification is not easier than regression estimation either in individual sense.

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值得尊敬

Lecture Notes in Computer Sciencehttp://image.papertrans.cn/c/image/232577.jpg

明智的人

Modifikation der Modellstrukturey theoretical work on boosting including analyses of AdaBoost’s training error and generalization error, connections between boosting and game theory, methods of estimating probabilities using boosting, and extensions of AdaBoost for multiclass classification problems. We also briefly mention some

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