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TCS
2011
14 years 4 months ago
Smart PAC-learners
The PAC-learning model is distribution-independent in the sense that the learner must reach a learning goal with a limited number of labeled random examples without any prior know...
Malte Darnstädt, Hans-Ulrich Simon
89
Voted
CVPR
2001
IEEE
15 years 11 months ago
A Weighted Non-Negative Matrix Factorization for Local Representations
This paper presents an improvement of the classical Non-negative Matrix Factorization (NMF) approach, for dealing with local representations of image objects. NMF, when applied to...
David Guillamet, Jordi Vitrià, Marco Bressa...
IDA
2006
Springer
14 years 9 months ago
Backward chaining rule induction
Exploring the vast number of possible feature interactions in domains such as gene expression microarray data is an onerous task. We describe Backward-Chaining Rule Induction (BCR...
Douglas H. Fisher, Mary E. Edgerton, Zhihua Chen, ...
CORR
2012
Springer
196views Education» more  CORR 2012»
13 years 5 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...
CVPR
2011
IEEE
14 years 5 months ago
Supervised Hierarchical Pitman-Yor Process for Natural Scene Segmentation
From conventional wisdom and empirical studies of annotated data, it has been shown that visual statistics such as object frequencies and segment sizes follow power law distributi...
Alex Shyr, Trevor Darrell, Michael Jordan, Raquel ...