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» Combining SVM Classifiers for Handwritten Digit Recognition
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CVPR
2007
IEEE
14 years 9 months ago
Adaptive Patch Features for Object Class Recognition with Learned Hierarchical Models
We present a hierarchical generative model for object recognition that is constructed by weakly-supervised learning. A key component is a novel, adaptive patch feature whose width...
Fabien Scalzo, Justus H. Piater
ICMI
2004
Springer
281views Biometrics» more  ICMI 2004»
14 years 26 days ago
Articulatory features for robust visual speech recognition
Visual information has been shown to improve the performance of speech recognition systems in noisy acoustic environments. However, most audio-visual speech recognizers rely on a ...
Kate Saenko, Trevor Darrell, James R. Glass
JMLR
2010
192views more  JMLR 2010»
13 years 2 months ago
Efficient Learning of Deep Boltzmann Machines
We present a new approximate inference algorithm for Deep Boltzmann Machines (DBM's), a generative model with many layers of hidden variables. The algorithm learns a separate...
Ruslan Salakhutdinov, Hugo Larochelle
IJCNN
2000
IEEE
13 years 11 months ago
Pose Classification Using Support Vector Machines
The field of human-computer interaction has been widely investigated in the last years, resulting in a variety of systems used in different application fields like virtual reality...
Edoardo Ardizzone, Antonio Chella, Roberto Pirrone
ICDAR
2005
IEEE
14 years 1 months ago
Hybrid Recognition for One Stroke Style Cursive Handwriting Characters
On-line handwriting recognition has continued to persist as a popular research field while pen computing applications are widely used in recent years. This paper proposes a novel ...
Teng Long, Lianwen Jin