Fretful
发表于 2025-3-23 13:23:58
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
发表于 2025-3-23 15:36:15
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
发表于 2025-3-23 18:02:00
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Expurgate
发表于 2025-3-23 23:05:07
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
束以马具
发表于 2025-3-24 02:44:50
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
发表于 2025-3-24 07:49:33
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
迁移
发表于 2025-3-24 13:33:52
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
发表于 2025-3-24 15:33:50
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ARY
发表于 2025-3-24 19:21:06
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paleolithic
发表于 2025-3-24 23:34:19
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.