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» Feature Selection and Effective Classifiers
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EMNLP
2009
13 years 6 months ago
Domain adaptive bootstrapping for named entity recognition
Bootstrapping is the process of improving the performance of a trained classifier by iteratively adding data that is labeled by the classifier itself to the training set, and retr...
Dan Wu, Wee Sun Lee, Nan Ye, Hai Leong Chieu
CVPR
2004
IEEE
14 years 11 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
CVPR
2006
IEEE
14 years 11 months ago
On-line Boosting and Vision
Boosting has become very popular in computer vision, showing impressive performance in detection and recognition tasks. Mainly off-line training methods have been used, which impl...
Helmut Grabner, Horst Bischof
ECAL
2005
Springer
14 years 2 months ago
The Quantitative Law of Effect is a Robust Emergent Property of an Evolutionary Algorithm for Reinforcement Learning
An evolutionary reinforcement-learning algorithm, the operation of which was not associated with an optimality condition, was instantiated in an artificial organism. The algorithm ...
J. J. McDowell, Zahra Ansari
ENGL
2008
101views more  ENGL 2008»
13 years 9 months ago
Identifying Perceptually Similar Languages Using Teager Energy Based Cepstrum
Language Identification (LID) refers to the task of identifying an unknown language from the test utterances. In this paper, a new method of feature extraction, viz., Teager Energy...
Hemant A. Patil, T. K. Basu