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» Learning with Few Examples by Transferring Feature Relevance
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AAAI
1998
14 years 11 months ago
Tree Based Discretization for Continuous State Space Reinforcement Learning
Reinforcement learning is an effective technique for learning action policies in discrete stochastic environments, but its efficiency can decay exponentially with the size of the ...
William T. B. Uther, Manuela M. Veloso
ECML
2006
Springer
15 years 1 months ago
Evaluating Feature Selection for SVMs in High Dimensions
We perform a systematic evaluation of feature selection (FS) methods for support vector machines (SVMs) using simulated high-dimensional data (up to 5000 dimensions). Several findi...
Roland Nilsson, José M. Peña, Johan ...
99
Voted
CVPR
2004
IEEE
15 years 11 months ago
Object-Based Image Retrieval Using the Statistical Structure of Images
We propose a new Bayesian approach to object-based image retrieval with relevance feedback. Although estimating the object posterior probability density from few examples seems in...
Derek Hoiem, Rahul Sukthankar, Henry Schneiderman,...
81
Voted
PAMI
2007
193views more  PAMI 2007»
14 years 9 months ago
Robust Object Recognition with Cortex-Like Mechanisms
—We introduce a new general framework for the recognition of complex visual scenes, which is motivated by biology: We describe a hierarchical system that closely follows the orga...
Thomas Serre, Lior Wolf, Stanley M. Bileschi, Maxi...
ICMCS
2009
IEEE
131views Multimedia» more  ICMCS 2009»
14 years 7 months ago
Web image mining using concept sensitive Markov stationary features
With the explosive growth of web resources, how to mine semantically relevant images efficiently becomes a challenging and necessary task. In this paper, we propose a concept sens...
Chunjie Zhang, Jing Liu, Hanqing Lu, Songde Ma