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

CLEFT

A Sequential Approximation Bound for Some Sample-Dependent Convex Optimization Problems with Applicons. This analysis is closely related to the regret bound framework in online learning. However we apply it to batch learning algorithms instead of online stochastic gradient decent methods. Applications of this analysis in some classification and regression problems will be illustrated.

自然环境

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.

否决

NIP

https://doi.org/10.1007/978-3-658-03986-8rons. As the main result we show that every reasonably sized standard network of radial basis function (RBF) neurons has VC dimension Ω([itW ] log .), where . is the number of parameters and . the number of nodes. This significantly improves the previously known linear bound. We also derive superlin

预示

, – Strukturen sozialer Unterstützungves predictions from a large set of . experts. Its goal is to predict almost as well as the best sequence of such experts chosen off-line by partitioning the training sequence into .+1 sections and then choosing the best expert for each section. We build on methods developed by Herbster and Warmuth

没血色

Forschungsgegenstand und Forschungsfragene and Warmuth’s Weighted Majority), for playing iterated games (including Freund and Schapire’s Hedge and MW, as well as the Λ-strategies of Hart and Mas-Colell), and for boosting (including AdaBoost) are special cases of a general decision strategy based on the notion of potential. By analyzing thi

blight

偶然

AGATE

青春期

The Sequential Analysis of Survival Data whether the vector’s components satisfy some linear inequality. In 1961, Chow [.] showed that any boolean perceptron is determined by the average or “center of gravity” of its “true” vectors (those that are mapped to 1). Moreover, this average distinguishes the function from any other boolean funct