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» Combining SVM Classifiers for Handwritten Digit Recognition
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CVPR
2007
IEEE
15 years 11 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»
15 years 3 months 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»
14 years 4 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
15 years 1 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
15 years 3 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