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» Covering Numbers for Support Vector Machines
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TNN
2008
182views more  TNN 2008»
15 years 3 months ago
Large-Scale Maximum Margin Discriminant Analysis Using Core Vector Machines
Abstract--Large-margin methods, such as support vector machines (SVMs), have been very successful in classification problems. Recently, maximum margin discriminant analysis (MMDA) ...
Ivor Wai-Hung Tsang, András Kocsor, James T...
ICML
2006
IEEE
16 years 4 months ago
Two-dimensional solution path for support vector regression
Recently, a very appealing approach was proposed to compute the entire solution path for support vector classification (SVC) with very low extra computational cost. This approach ...
Gang Wang, Dit-Yan Yeung, Frederick H. Lochovsky
ICIP
2009
IEEE
15 years 1 months ago
Efficient reduction of support vectors in kernel-based methods
Kernel-based methods, e.g., support vector machine (SVM), produce high classification performances. However, the computation becomes time-consuming as the number of the vectors su...
Takumi Kobayashi, Nobuyuki Otsu
NIPS
2003
15 years 5 months ago
Margin Maximizing Loss Functions
Margin maximizing properties play an important role in the analysis of classi£cation models, such as boosting and support vector machines. Margin maximization is theoretically in...
Saharon Rosset, Ji Zhu, Trevor Hastie
ESANN
2004
15 years 5 months ago
Using classification to determine the number of finger strokes on a multi-touch tactile device
On certain types of multi-touch touchpads, determining the number of finger stroke is a non-trivial problem. We investigate the application of several classification algorithms to ...
Caspar von Wrede, Pavel Laskov