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» A theory of learning with similarity functions
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KR
1992
Springer
15 years 3 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
ATAL
2006
Springer
15 years 3 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 9 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 6 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 6 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