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98
Voted
CVPR
2010
IEEE
15 years 9 months ago
2010
IEEE
For object category recognition to scale beyond a small number of classes, it is important that algorithms be able to learn from a small amount of labeled data per additional clas...
116
Voted
EUSFLAT
2003
15 years 2 months ago
2003
This paper proposes a method for Bayesian networks that handles uncertainty and discretization of continuous variables when learning the networks from a database of cases. The dat...
125
Voted
SDM
2007
SIAM
15 years 2 months ago
2007
SIAM
Manifold learning is an effective methodology for extracting nonlinear structures from high-dimensional data with many applications in image analysis, computer vision, text data a...
120
Voted
JMLR
2012
13 years 3 months ago
2012
We study the problem of learning a group of principal tasks using a group of auxiliary tasks, unrelated to the principal ones. In many applications, joint learning of unrelated ta...
107
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GECCO
2005
Springer
15 years 6 months ago
2005
Springer
XCS is a stochastic algorithm, so it does not guarantee to produce the same results when run with the same input. When interpretability matters, obtaining a single, stable result ...