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ICCV
2005
IEEE
14 years 11 months ago
Probabilistic Boosting-Tree: Learning Discriminative Models for Classification, Recognition, and Clustering
In this paper, a new learning framework?probabilistic boosting-tree (PBT), is proposed for learning two-class and multi-class discriminative models. In the learning stage, the pro...
Zhuowen Tu
ICDE
2007
IEEE
125views Database» more  ICDE 2007»
14 years 11 months ago
Ontology-Based Constraint Recognition for Free-Form Service Requests
Automatic recognition and formalization of constraints from free-form service requests is a challenging problem. Its resolution would go a long way toward allowing users to make r...
Muhammed Al-Muhammed, David W. Embley
ICASSP
2009
IEEE
14 years 4 months ago
Using collective information in semi-supervised learning for speech recognition
Training accurate acoustic models typically requires a large amount of transcribed data, which can be expensive to obtain. In this paper, we describe a novel semi-supervised learn...
Balakrishnan Varadarajan, Dong Yu, Li Deng, Alex A...
ICASSP
2008
IEEE
14 years 4 months ago
A study of using locality preserving projections for feature extraction in speech recognition
This paper presents a new approach to feature analysis in automatic speech recognition (ASR) based on locality preserving projections (LPP). LPP is a manifold based dimensionality...
Yun Tang, Richard Rose
SEMCO
2007
IEEE
14 years 3 months ago
Large-Margin Discriminative Training of Hidden Markov Models for Speech Recognition
Discriminative training has been a leading factor for improving automatic speech recognition (ASR) performance over the last decade. The traditional discriminative training, howev...
Dong Yu, Li Deng