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» Data selection for support vector machine classifiers
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ICML
2006
IEEE
15 years 11 months ago
How boosting the margin can also boost classifier complexity
Boosting methods are known not to usually overfit training data even as the size of the generated classifiers becomes large. Schapire et al. attempted to explain this phenomenon i...
Lev Reyzin, Robert E. Schapire
ACL
2006
14 years 11 months ago
Semantic Parsing with Structured SVM Ensemble Classification Models
We present a learning framework for structured support vector models in which boosting and bagging methods are used to construct ensemble models. We also propose a selection metho...
Minh Le Nguyen, Akira Shimazu, Xuan Hieu Phan
NECO
2008
112views more  NECO 2008»
14 years 10 months ago
Second-Order SMO Improves SVM Online and Active Learning
Iterative learning algorithms that approximate the solution of support vector machines (SVMs) have two potential advantages. First, they allow for online and active learning. Seco...
Tobias Glasmachers, Christian Igel
CVPR
2008
IEEE
16 years 3 days ago
A NURBS-based spectral reflectance descriptor with applications in computer vision and pattern recognition
In this paper, we present a surface reflectance descriptor based on the control points resulting from the interpolation of Non-Uniform Rational B-Spline (NURBS) curves to multispe...
Cong Phuoc Huynh, Antonio Robles-Kelly
AVSS
2009
IEEE
15 years 4 months ago
Towards Generic Detection of Unusual Events in Video Surveillance
—In this paper, we consider the challenging problem of unusual event detection in video surveillance systems. The proposed approach makes a step toward generic and automatic dete...
Ivan Ivanov, Frédéric Dufaux, Thien ...