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» Training of Classifiers Using Virtual Samples Only
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ICDAR
2003
IEEE
14 years 24 days ago
Gabor Filter Based Multi-class Classifier for Scanned Document Images
When scanning documents with a large number of pages such as books, it is often feasible to provide a minimal number of training samples to personalize the system to compensate fo...
Huanfeng Ma, David S. Doermann
CVPR
2009
IEEE
13 years 11 months ago
Efficiently training a better visual detector with sparse eigenvectors
Face detection plays an important role in many vision applications. Since Viola and Jones [1] proposed the first real-time AdaBoost based object detection system, much effort has ...
Sakrapee Paisitkriangkrai, Chunhua Shen, Jian Zhan...
ICML
2004
IEEE
14 years 8 months ago
Learning and evaluating classifiers under sample selection bias
Classifier learning methods commonly assume that the training data consist of randomly drawn examples from the same distribution as the test examples about which the learned model...
Bianca Zadrozny
CVPR
2004
IEEE
14 years 9 months ago
Random Sampling LDA for Face Recognition
Linear Discriminant Analysis (LDA) is a popular feature extraction technique for face recognition. However, It often suffers from the small sample size problem when dealing with t...
Xiaogang Wang, Xiaoou Tang
ICCV
2003
IEEE
14 years 9 months ago
Unsupervised Improvement of Visual Detectors using Co-Training
One significant challenge in the construction of visual detection systems is the acquisition of sufficient labeled data. This paper describes a new technique for training visual d...
Anat Levin, Paul A. Viola, Yoav Freund