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» Covering Numbers for Support Vector Machines
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COLT
2004
Springer
15 years 5 months ago
Sparseness Versus Estimating Conditional Probabilities: Some Asymptotic Results
One of the nice properties of kernel classifiers such as SVMs is that they often produce sparse solutions. However, the decision functions of these classifiers cannot always be u...
Peter L. Bartlett, Ambuj Tewari
SIGIR
1999
ACM
15 years 4 months ago
A Re-Examination of Text Categorization Methods
This paper reports a controlled study with statistical signi cance tests on ve text categorization methods: the Support Vector Machines (SVM), a k-Nearest Neighbor (kNN) classi er...
Yiming Yang, Xin Liu
DIS
2008
Springer
15 years 1 months ago
String Kernels Based on Variable-Length-Don't-Care Patterns
Abstract. We propose a new string kernel based on variable-lengthdon't-care patterns (VLDC patterns). A VLDC pattern is an element of ({}) , where is an alphabet and is the ...
Kazuyuki Narisawa, Hideo Bannai, Kohei Hatano, Shu...
MVA
2007
146views Computer Vision» more  MVA 2007»
15 years 1 months ago
A SVM Based Method to Detect Color Shift Defects in IC Packages
Automated Visual Inspection (AVI) is an essential part in the manufacturing process of Integrated Circuit (IC) packages. Contamination a common defect type found in IC packages ap...
R. M. C. B. Ratnayake, Craig Hicks, M. A. Akbari
JMLR
2006
143views more  JMLR 2006»
14 years 11 months ago
Consistency and Convergence Rates of One-Class SVMs and Related Algorithms
We determine the asymptotic behaviour of the function computed by support vector machines (SVM) and related algorithms that minimize a regularized empirical convex loss function i...
Régis Vert, Jean-Philippe Vert