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» Approximation Methods for Supervised Learning
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RSKT
2009
Springer
16 years 28 days 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
JMLR
2010
99views more  JMLR 2010»
15 years 1 months ago
An Efficient Explanation of Individual Classifications using Game Theory
We present a general method for explaining individual predictions of classification models. The method is based on fundamental concepts from coalitional game theory and prediction...
Erik Strumbelj, Igor Kononenko
NIPS
1998
15 years 7 months ago
Lazy Learning Meets the Recursive Least Squares Algorithm
Lazy learning is a memory-based technique that, once a query is received, extracts a prediction interpolating locally the neighboring examples of the query which are considered re...
Mauro Birattari, Gianluca Bontempi, Hugues Bersini
CORR
2012
Springer
196views Education» more  CORR 2012»
14 years 2 months ago
PAC-Bayesian Policy Evaluation for Reinforcement Learning
Bayesian priors offer a compact yet general means of incorporating domain knowledge into many learning tasks. The correctness of the Bayesian analysis and inference, however, lar...
Mahdi Milani Fard, Joelle Pineau, Csaba Szepesv&aa...
MICCAI
2007
Springer
16 years 7 months ago
Active-Contour-Based Image Segmentation Using Machine Learning Techniques
Abstract. We introduce a non-linear shape prior for the deformable model framework that we learn from a set of shape samples using recent manifold learning techniques. We model a c...
Patrick Etyngier, Florent Ségonne, Renaud K...