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» On learning with dissimilarity functions
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NIPS
2008
15 years 8 months ago
Regularized Policy Iteration
In this paper we consider approximate policy-iteration-based reinforcement learning algorithms. In order to implement a flexible function approximation scheme we propose the use o...
Amir Massoud Farahmand, Mohammad Ghavamzadeh, Csab...
NIPS
2007
15 years 8 months ago
On higher-order perceptron algorithms
A new algorithm for on-line learning linear-threshold functions is proposed which efficiently combines second-order statistics about the data with the ”logarithmic behavior” ...
Claudio Gentile, Fabio Vitale, Cristian Brotto
GECCO
2008
Springer
170views Optimization» more  GECCO 2008»
15 years 7 months ago
Evolving prediction weights using evolution strategy
The evolution strategy is one of the strongest evolutionary algorithms for optimizing real-value vectors. In this paper, we study how to use it for the evolution of prediction wei...
Trung Hau Tran, Cédric Sanza, Yves Duthen
SDM
2012
SIAM
216views Data Mining» more  SDM 2012»
13 years 9 months ago
Feature Selection "Tomography" - Illustrating that Optimal Feature Filtering is Hopelessly Ungeneralizable
:  Feature Selection “Tomography” - Illustrating that Optimal Feature Filtering is Hopelessly Ungeneralizable George Forman HP Laboratories HPL-2010-19R1 Feature selection; ...
George Forman
ICML
2005
IEEE
16 years 7 months ago
Clustering through ranking on manifolds
Clustering aims to find useful hidden structures in data. In this paper we present a new clustering algorithm that builds upon the consistency method (Zhou, et.al., 2003), a semi-...
Markus Breitenbach, Gregory Z. Grudic