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» Discriminative Direction for Kernel Classifiers
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ACL
2007
13 years 11 months ago
A discriminative language model with pseudo-negative samples
In this paper, we propose a novel discriminative language model, which can be applied quite generally. Compared to the well known N-gram language models, discriminative language m...
Daisuke Okanohara, Jun-ichi Tsujii
CVPR
2005
IEEE
14 years 12 months ago
Local Discriminant Embedding and Its Variants
We present a new approach, called local discriminant embedding (LDE), to manifold learning and pattern classification. In our framework, the neighbor and class relations of data a...
Hwann-Tzong Chen, Huang-Wei Chang, Tyng-Luh Liu
CVPR
2010
IEEE
14 years 6 months ago
Discriminative Clustering for Image Co-segmentation
Purely bottom-up, unsupervised segmentation of a single image into two segments remains a challenging task for computer vision. The co-segmentation problem is the process of joi...
Armand Joulin, Francis Bach, Jean Ponce
CVPR
2010
IEEE
14 years 1 months ago
Large-Scale Image Categorization with Explicit Data Embedding
Kernel machines rely on an implicit mapping of the data such that non-linear classification in the original space corresponds to linear classification in the new space. As kernel ...
Florent Perronnin, Jorge Sanchez, Yan Liu
EVOW
2006
Springer
14 years 1 months ago
Human Papillomavirus Risk Type Classification from Protein Sequences Using Support Vector Machines
Infection by the human papillomavirus (HPV) is associated with the development of cervical cancer. HPV can be classified to highand low-risk type according to its malignant potenti...
Sun Kim, Byoung-Tak Zhang