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» On learning with dissimilarity functions
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ICML
2010
IEEE
13 years 10 months ago
Efficient Reinforcement Learning with Multiple Reward Functions for Randomized Controlled Trial Analysis
We introduce new, efficient algorithms for value iteration with multiple reward functions and continuous state. We also give an algorithm for finding the set of all nondominated a...
Daniel J. Lizotte, Michael H. Bowling, Susan A. Mu...
CEC
2005
IEEE
14 years 2 months ago
XCS with computed prediction for the learning of Boolean functions
Computed prediction represents a major shift in learning classifier system research. XCS with computed prediction, based on linear approximators, has been applied so far to functi...
Pier Luca Lanzi, Daniele Loiacono, Stewart W. Wils...
CORR
2010
Springer
92views Education» more  CORR 2010»
13 years 9 months ago
Hardness Results for Agnostically Learning Low-Degree Polynomial Threshold Functions
Hardness results for maximum agreement problems have close connections to hardness results for proper learning in computational learning theory. In this paper we prove two hardnes...
Ilias Diakonikolas, Ryan O'Donnell, Rocco A. Serve...
ICPR
2000
IEEE
14 years 10 months ago
On Gaussian Radial Basis Function Approximations: Interpretation, Extensions, and Learning Strategies
In this paper we focus on an interpretation of Gaussian radial basis functions (GRBF) which motivates extensions and learning strategies. Specifically, we show that GRBF regressio...
Mário A. T. Figueiredo
ICML
2000
IEEE
14 years 10 months ago
Learning Subjective Functions with Large Margins
In manyoptimization and decision problems the objective function can be expressed as a linear combinationof competingcriteria, the weights of whichspecify the relative importanceo...
Claude-Nicolas Fiechter, Seth Rogers