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ECML
2007
Springer
13 years 9 months ago
Fast Optimization Methods for L1 Regularization: A Comparative Study and Two New Approaches
L1 regularization is effective for feature selection, but the resulting optimization is challenging due to the non-differentiability of the 1-norm. In this paper we compare state...
Mark Schmidt, Glenn Fung, Rómer Rosales
ICASSP
2009
IEEE
13 years 10 months ago
Speech emotion recognition via a max-margin framework incorporating a loss function based on the Watson and Tellegen's emotion m
This paper considers a method for speech emotion recognition by a max-margin framework incorporating a loss function based on a well-known model called the Watson and Tellegen’s...
Sungrack Yun, Chang D. Yoo
ICML
2008
IEEE
14 years 4 months ago
Listwise approach to learning to rank: theory and algorithm
This paper aims to conduct a study on the listwise approach to learning to rank. The listwise approach learns a ranking function by taking individual lists as instances and minimi...
Fen Xia, Tie-Yan Liu, Jue Wang, Wensheng Zhang, Ha...