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» A Framework for Multiple-Instance Learning
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163
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PAMI
2006
215views more  PAMI 2006»
15 years 4 months ago
Bayesian Feature and Model Selection for Gaussian Mixture Models
We present a Bayesian method for mixture model training that simultaneously treats the feature selection and the model selection problem. The method is based on the integration of ...
Constantinos Constantinopoulos, Michalis K. Titsia...
137
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IJLT
2007
67views more  IJLT 2007»
15 years 4 months ago
Differentiated e-learning: five approaches through instructional technology
: Differentiated instruction is an approach to teaching that acknowledges people have multiple paths for learning and for making sense of ideas. In e-learning, differentiated instr...
Kathleen Scalise
156
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APIN
2004
131views more  APIN 2004»
15 years 4 months ago
Rough Self Organizing Map
A rough self-organizing map (RSOM) with fuzzy discretization of feature space is described here. Discernibility reducts obtained using rough set theory are used to extract domain k...
Sankar K. Pal, Biswarup Dasgupta, Pabitra Mitra
93
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JMLR
2002
75views more  JMLR 2002»
15 years 4 months ago
Stability and Generalization
We define notions of stability for learning algorithms and show how to use these notions to derive generalization error bounds based on the empirical error and the leave-one-out e...
Olivier Bousquet, André Elisseeff
152
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ICCV
2011
IEEE
14 years 4 months ago
Regression from Local Features for Viewpoint and Pose Estimation
In this paper we propose a framework for learning a regression function form a set of local features in an image. The regression is learned from an embedded representation that re...
Marwan Torki, Ahmed Elgammal