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» Self Bounding Learning Algorithms
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STACS
1999
Springer
14 years 1 months ago
A Complete and Tight Average-Case Analysis of Learning Monomials
Abstract. We advocate to analyze the average complexity of learning problems. An appropriate framework for this purpose is introduced. Based on it we consider the problem of learni...
Rüdiger Reischuk, Thomas Zeugmann
COLT
2004
Springer
14 years 1 months ago
Regret Bounds for Hierarchical Classification with Linear-Threshold Functions
We study the problem of classifying data in a given taxonomy when classifications associated with multiple and/or partial paths are allowed. We introduce an incremental algorithm u...
Nicolò Cesa-Bianchi, Alex Conconi, Claudio ...
NIPS
2004
13 years 11 months ago
Mistake Bounds for Maximum Entropy Discrimination
We establish a mistake bound for an ensemble method for classification based on maximizing the entropy of voting weights subject to margin constraints. The bound is the same as a ...
Philip M. Long, Xinyu Wu
GECCO
2005
Springer
220views Optimization» more  GECCO 2005»
14 years 3 months ago
Scale invariant pareto optimality: a meta--formalism for characterizing and modeling cooperativity in evolutionary systems
This article describes a mathematical framework for characterizing cooperativity in complex systems subject to evolutionary pressures. This framework uses three foundational compo...
Mark Fleischer
JMLR
2002
140views more  JMLR 2002»
13 years 9 months ago
On Boosting with Polynomially Bounded Distributions
We construct a framework which allows an algorithm to turn the distributions produced by some boosting algorithms into polynomially smooth distributions (w.r.t. the PAC oracle...
Nader H. Bshouty, Dmitry Gavinsky