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AVBPA
2003
Springer
133views Biometrics» more  AVBPA 2003»
13 years 11 months ago
LUT-Based Adaboost for Gender Classification
There are two main approaches to the problem of gender classification, Support Vector Machines (SVMs) and Adaboost learning methods, of which SVMs are better in correct rate but ar...
Bo Wu, Haizhou Ai, Chang Huang
ICCV
2005
IEEE
14 years 9 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
MICCAI
2007
Springer
14 years 8 months ago
High Throughput Analysis of Breast Cancer Specimens on the Grid
Breast cancer accounts for about 30% of all cancers and 15% of all cancer deaths in women in the United States. Advances in computer assisted diagnosis (CAD) holds promise for earl...
Lin Yang, Wenjin Chen, Peter Meer, Gratian Salaru,...
ICML
2004
IEEE
14 years 8 months ago
Training conditional random fields via gradient tree boosting
Conditional Random Fields (CRFs; Lafferty, McCallum, & Pereira, 2001) provide a flexible and powerful model for learning to assign labels to elements of sequences in such appl...
Thomas G. Dietterich, Adam Ashenfelter, Yaroslav B...
PR
2006
164views more  PR 2006»
13 years 7 months ago
Locally linear metric adaptation with application to semi-supervised clustering and image retrieval
Many computer vision and pattern recognition algorithms are very sensitive to the choice of an appropriate distance metric. Some recent research sought to address a variant of the...
Hong Chang, Dit-Yan Yeung