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PAMI
2010
132views more  PAMI 2010»
13 years 6 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
ICASSP
2008
IEEE
14 years 2 months ago
Using corpus and knowledge-based similarity measure in Maximum Marginal Relevance for meeting summarization
MMR (Maximum Marginal Relevance) is widely used in summarization for its simplicity and efficacy, and has been demonstrated to achieve comparable performance to other approaches ...
Shasha Xie, Yang Liu
CVPR
2005
IEEE
14 years 1 months ago
Nonlinear Face Recognition Based on Maximum Average Margin Criterion
This paper proposes a novel nonlinear discriminant analysis method named by Kernerlized Maximum Average Margin Criterion (KMAMC), which has combined the idea of Support Vector Mac...
Baochang Zhang, Xilin Chen, Shiguang Shan, Wen Gao
CSDA
2007
136views more  CSDA 2007»
13 years 7 months ago
A note on iterative marginal optimization: a simple algorithm for maximum rank correlation estimation
The maximum rank correlation (MRC) estimator was originally studied by Han [1987. Nonparametric analysis of a generalized regression model. J. Econometrics 35, 303–316] and Sher...
Hansheng Wang
ECCV
2010
Springer
14 years 26 days ago
Maximum Margin Distance Learning for Dynamic Texture Recognition
The range space of dynamic textures spans spatiotemporal phenomena that vary along three fundamental dimensions: spatial texture, spatial texture layout, and dynamics. By describin...