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» On the Complexity of Function Learning
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PRL
2008
82views more  PRL 2008»
15 years 4 months ago
Optimistic pruning for multiple instance learning
This paper introduces a simple evaluation function for multiple instance learning that admits an optimistic pruning strategy. We demonstrate comparable results to state of the art...
Amy McGovern, David Jensen
EWRL
2008
15 years 6 months ago
Bayesian Reward Filtering
A wide variety of function approximation schemes have been applied to reinforcement learning. However, Bayesian filtering approaches, which have been shown efficient in other field...
Matthieu Geist, Olivier Pietquin, Gabriel Fricout
ILP
2005
Springer
15 years 10 months ago
Predicate Selection for Structural Decision Trees
Abstract. We study predicate selection functions (also known as splitting rules) for structural decision trees and propose two improvements to existing schemes. The first is in cl...
Kee Siong Ng, John W. Lloyd
ESANN
1998
15 years 5 months ago
Wavelet interpolation networks
We describe a new approach to real time learning of unknown functions based on an interpolating wavelet estimation. We choose a subfamily of a wavelet basis relying on nested hiera...
Christophe P. Bernard, Stéphane Mallat, Jea...
JMLR
2006
79views more  JMLR 2006»
15 years 4 months ago
Estimation of Gradients and Coordinate Covariation in Classification
We introduce an algorithm that simultaneously estimates a classification function as well as its gradient in the supervised learning framework. The motivation for the algorithm is...
Sayan Mukherjee, Qiang Wu