Titlebook: Computational Learning Theory; 14th Annual Conferen David Helmbold,Bob Williamson Conference proceedings 2001 Springer-Verlag Berlin Heidel

Fretful

On the Synthesis of Strategies Identifying Recursive Functions, of its output values. Uniform learning is concerned with the design of single programs solving infinitely many classical learning problems. For that purpose the program reads a description of an identification problem and is supposed to construct a technique for solving the particular problem..As c

VERT

Intrinsic Complexity of Learning Geometrical Concepts from Positive Data, strategy learning such geometrical concept can be viewed as a sequence of . strategies. Thus, the length of such a sequence together with complexities of primitive strategies used can be regarded as complexity of learning the concept in question. We obtained best possible lower and upper bounds on

Lasting

Expurgate

Discrete Prediction Games with Arbitrary Feedback and Loss (Extended Abstract),on the predicted values. This setting can be seen as a generalization of the classical multi-armed bandit problem and accommodates as a special case a natural bandwidth allocation problem. According to the approach adopted by many authors, we give up any statistical assumption on the sequence to be

束以馬具

Rademacher and Gaussian Complexities: Risk Bounds and Structural Results,cision theoretic setting, we prove general risk bounds in terms of these complexities. We consider function classes that can be expressed as combinations of functions from basis classes and show how the Rademacher and gaussian complexities of such a function class can be bounded in terms of the comp

FLIP

Further Explanation of the Effectiveness of Voting Methods: The Game between Margins and Weights,ss .. The algorithms of combining simple classifiers into a complex one, such as boosting and bagging, have attracted a lot of attention. We obtain new sharper bounds on the generalization error of combined classifiers that take into account both the empirical distribution of “classification margins

遷移

Geometric Methods in the Analysis of Glivenko-Cantelli Classes,ko-Cantelli classes for . in terms of the fat-shatteringdimension of the class, which does not depend on the size of the sample. Usingthe new bound, we improve the known sample complexity estimates and bound the size of the Sufficient Statistics needed for Glivenko-Cantelli classes.

construct

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paleolithic

The Sequential Analysis of Survival Datalean perceptron that is accurate to within error ε (the fraction of misclassified vectors). This provides a mildly super-polynomial bound on the sample complexity of learning boolean perceptrons in the “restricted focus of attention” setting. In the process we also find some interesting geometrical properties of the vertices of the unit hypercube.