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» Feature selection for ranking using boosted trees
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PRL
1998
132views more  PRL 1998»
15 years 3 months ago
Unsupervised feature selection using a neuro-fuzzy approach
A neuro-fuzzy methodology is described which involves connectionist minimization of a fuzzy feature evaluation index with unsupervised training. The concept of a ¯exible membersh...
Jayanta Basak, Rajat K. De, Sankar K. Pal
184
Voted
NIPS
2003
15 years 5 months ago
Learning a Rare Event Detection Cascade by Direct Feature Selection
Face detection is a canonical example of a rare event detection problem, in which target patterns occur with much lower frequency than nontargets. Out of millions of face-sized wi...
Jianxin Wu, James M. Rehg, Matthew D. Mullin
139
Voted
AI
2004
Springer
15 years 3 months ago
A selective sampling approach to active feature selection
Feature selection, as a preprocessing step to machine learning, has been very effective in reducing dimensionality, removing irrelevant data, increasing learning accuracy, and imp...
Huan Liu, Hiroshi Motoda, Lei Yu
125
Voted
CVPR
2004
IEEE
16 years 5 months ago
Asymmetrically Boosted HMM for Speech Reading
Speech reading, also known as lip reading, is aimed at extracting visual cues of lip and facial movements to aid in recognition of speech. The main hurdle for speech reading is th...
Pei Yin, Irfan A. Essa, James M. Rehg
126
Voted
MM
2010
ACM
174views Multimedia» more  MM 2010»
15 years 4 months ago
Personalized photograph ranking and selection system
In this paper, we propose a novel personalized ranking system for amateur photographs. Although some of the features used in our system are similar to previous work, new features,...
Che-Hua Yeh, Yuan-Chen Ho, Brian A. Barsky, Ming O...