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» Training of Classifiers Using Virtual Samples Only
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ICPR
2008
IEEE
14 years 3 months ago
Pre-extracting method for SVM classification based on the non-parametric K-NN rule
With the increase of the training set’s size, the efficiency of support vector machine (SVM) classifier will be confined. To solve such a problem, a novel preextracting method f...
Deqiang Han, Chongzhao Han, Yi Yang, Yu Liu, Wenta...
DAGM
2009
Springer
14 years 28 days ago
Training for Task Specific Keypoint Detection
In this paper, we show that a better performance can be achieved by training a keypoint detector to only find those points that are suitable to the needs of the given task. We demo...
Christoph Strecha, Albrecht Lindner, Karim Ali, Pa...
ICML
2007
IEEE
14 years 9 months ago
Minimum reference set based feature selection for small sample classifications
We address feature selection problems for classification of small samples and high dimensionality. A practical example is microarray-based cancer classification problems, where sa...
Xue-wen Chen, Jong Cheol Jeong
FGR
2004
IEEE
132views Biometrics» more  FGR 2004»
14 years 23 days ago
Expand Training Set for Face Detection by GA Re-sampling
Data collection for both training and testing a classifier is a tedious but essential step towards face detection and recognition. All of the statistical methods suffer from this ...
Jie Chen, Xilin Chen, Wen Gao
NAACL
2003
13 years 10 months ago
Active Learning for Classifying Phone Sequences from Unsupervised Phonotactic Models
This paper describes an application of active learning methods to the classification of phone strings recognized using unsupervised phonotactic models. The only training data req...
Shona Douglas