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

GULLY

Hormones

Rademacher and Gaussian Complexities: Risk Bounds and Structural Results,ons 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 complexity of the basis classes.We give examples of the application of these techniques in finding data-dependent risk bounds for decision trees, neural networks and support vector machines.

Sputum

Further Explanation of the Effectiveness of Voting Methods: The Game between Margins and Weights,” and the “approximate dimension” of the classifier, which is defined in terms of weights assigned to base classifiers by a voting algorithm. We study the performance of these bounds in several experiments with learning algorithms.

Affirm

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Eine US-amerikanische Zivilgesellschaft?, in both cases turn out to be much lower than those provided by natural learning strategies. Another surprising result is that learning intersections of open semi-hulls (and their complements) turns out to be easier than learning open semi-hulls themselves.

Kinetic

Intrinsic Complexity of Learning Geometrical Concepts from Positive Data, in both cases turn out to be much lower than those provided by natural learning strategies. Another surprising result is that learning intersections of open semi-hulls (and their complements) turns out to be easier than learning open semi-hulls themselves.

Asperity

How Many Queries Are Needed to Learn One Bit of Information?,nd learning by counterexamples (equivalence queries alone). These parameters are finally used to characterize the additional power provided by membership queries (compared to the power of equivalence queries alone). All investigations are purely information-theoretic and ignore computational issues.

LAVE

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Tracking a Small Set of Experts by Mixing Past Posteriors,for choosing the best expert in each section we first pay log (.) bits in the bounds for identifying the pool of . experts and then logm bits per new section. In the bounds we also pay twice for encoding the boundaries of the sections.

Paraplegia

Ultraconservative Online Algorithms for Multiclass Problems, We then discuss a specific online algorithm that seeks a set of prototypes which have a small norm. The resulting algorithm, which we term MIRA (for Margin Infused Relaxed Algorithm) is ultraconservative as well. We derive mistake bounds for all the algorithms and provide further analysis of MIRA u