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» A theory of learning with similarity functions
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KR
1992
Springer
15 years 5 months ago
Learning Useful Horn Approximations
While the task of answering queries from an arbitrary propositional theory is intractable in general, it can typicallybe performed e ciently if the theory is Horn. This suggests t...
Russell Greiner, Dale Schuurmans
96
Voted
ATAL
2006
Springer
15 years 5 months ago
Probabilistic policy reuse in a reinforcement learning agent
We contribute Policy Reuse as a technique to improve a reinforcement learning agent with guidance from past learned similar policies. Our method relies on using the past policies ...
Fernando Fernández, Manuela M. Veloso
ICASSP
2010
IEEE
14 years 11 months ago
A supervisory approach to semi-supervised clustering
We propose a new approach to semi-supervised clustering that utilizes boosting to simultaneously learn both a similarity measure and a clustering of the data from given instancele...
Bryan Conroy, Yongxin Taylor Xi, Peter J. Ramadge
SIBGRAPI
2009
IEEE
15 years 8 months ago
Hermite Interpolation of Implicit Surfaces with Radial Basis Functions
—We present the Hermite radial basis function (HRBF) implicits method to compute a global implicit function which interpolates scattered multivariate Hermite data (unstructured p...
Ives Macedo, Joao Paulo Gois, Luiz Velho
RSKT
2009
Springer
15 years 8 months ago
Learning Optimal Parameters in Decision-Theoretic Rough Sets
A game-theoretic approach for learning optimal parameter values for probabilistic rough set regions is presented. The parameters can be used to define approximation regions in a p...
Joseph P. Herbert, Jingtao Yao