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141
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IR
2010
15 years 1 months ago
Learning to rank with (a lot of) word features
In this article we present Supervised Semantic Indexing (SSI) which defines a class of nonlinear (quadratic) models that are discriminatively trained to directly map from the word...
Bing Bai, Jason Weston, David Grangier, Ronan Coll...
ECCC
2006
96views more  ECCC 2006»
15 years 2 months ago
When Does Greedy Learning of Relevant Features Succeed? --- A Fourier-based Characterization ---
Detecting the relevant attributes of an unknown target concept is an important and well studied problem in algorithmic learning. Simple greedy strategies have been proposed that s...
Jan Arpe, Rüdiger Reischuk
104
Voted
ICML
2003
IEEE
16 years 3 months ago
Adaptive Feature-Space Conformal Transformation for Imbalanced-Data Learning
When the training instances of the target class are heavily outnumbered by non-target training instances, SVMs can be ineffective in determining the class boundary. To remedy this...
Gang Wu, Edward Y. Chang
118
Voted
ISNN
2007
Springer
15 years 8 months ago
Extensions of Manifold Learning Algorithms in Kernel Feature Space
Manifold learning algorithms have been proven to be capable of discovering some nonlinear structures. However, it is hard for them to extend to test set directly. In this paper, a ...
Yaoliang Yu, Peng Guan, Liming Zhang
126
Voted
ICIP
2007
IEEE
16 years 4 months ago
MuFeSaC: Learning When to Use Which Feature Detector
Interest point detectors are the starting point in image analysis for depth estimation using epipolar geometry and camera ego-motion estimation. With several detectors defined in ...
Sreenivas R. Sukumar, David L. Page, Hamparsum Boz...