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» A Support Vector Clustering Method
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149
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NPL
2002
168views more  NPL 2002»
15 years 3 months ago
Reduced Rank Kernel Ridge Regression
Ridge regression is a classical statistical technique that attempts to address the bias-variance trade-off in the design of linear regression models. A reformulation of ridge regr...
Gavin C. Cawley, Nicola L. C. Talbot
146
Voted
PAMI
2010
132views more  PAMI 2010»
15 years 2 months ago
Maximum Likelihood Model Selection for 1-Norm Soft Margin SVMs with Multiple Parameters
—Adapting the hyperparameters of support vector machines (SVMs) is a challenging model selection problem, especially when flexible kernels are to be adapted and data are scarce....
Tobias Glasmachers, Christian Igel
135
Voted
PKDD
2010
Springer
160views Data Mining» more  PKDD 2010»
15 years 2 months ago
Entropy and Margin Maximization for Structured Output Learning
Abstract. We consider the problem of training discriminative structured output predictors, such as conditional random fields (CRFs) and structured support vector machines (SSVMs)....
Patrick Pletscher, Cheng Soon Ong, Joachim M. Buhm...
126
Voted
ICIP
2010
IEEE
15 years 1 months ago
Adaptive motion model selection using a cubic spline based estimation framework
A block based video coder that supports multiple motion models is proposed. Apart from the typical translational motion model, we employ parametric models to more accurately repre...
Haricharan Lakshman, Heiko Schwarz, Thomas Wiegand
128
Voted
ICASSP
2009
IEEE
15 years 1 months ago
Image spam filtering using Fourier-Mellin invariant features
Image spam is a new obfuscating method which spammers invented to more effectively bypass conventional text based spam filters. In this paper, a framework for filtering image spam...
Haiqiang Zuo, Xi Li, Ou Wu, Weiming Hu, Guan Luo