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» Unsupervised Learning of Models for Recognition
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ICASSP
2009
IEEE
15 years 10 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...
146
Voted
IJDAR
2010
169views more  IJDAR 2010»
15 years 2 months ago
A Bayesian network for combining descriptors: application to symbol recognition
Inthispaper,weproposeadescriptorcombination method, which enables to improve significantly the recognition rate compared to the recognition rates obtained by each descriptor. This ...
Sabine Barrat, Salvatore Tabbone
CVPR
2007
IEEE
16 years 5 months ago
Learning a Spatially Smooth Subspace for Face Recognition
Subspace learning based face recognition methods have attracted considerable interests in recently years, including Principal Component Analysis (PCA), Linear Discriminant Analysi...
Deng Cai, Xiaofei He, Yuxiao Hu, Jiawei Han, Thoma...
134
Voted
BVAI
2005
Springer
15 years 9 months ago
Learning Location Invariance for Object Recognition and Localization
A visual system not only needs to recognize a stimulus, it also needs to find the location of the stimulus. In this paper, we present a neural network model that is able to genera...
Gwendid T. van der Voort van der Kleij, Frank van ...
145
Voted
CVPR
2010
IEEE
15 years 12 months ago
Learning a Hierarchy of Discriminative Space-Time Neighborhood Features for Human Action Recognition
Recent work shows how to use local spatio-temporal features to learn models of realistic human actions from video. However, existing methods typically rely on a predefined spatial...
Adriana Kovashka, Kristen Grauman