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JMLR
2010
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13 years 4 months ago
Increasing Feature Selection Accuracy for L1 Regularized Linear Models
L1 (also referred to as the 1-norm or Lasso) penalty based formulations have been shown to be effective in problem domains when noisy features are present. However, the L1 penalty...
Abhishek Jaiantilal, Gregory Z. Grudic
ACL
2010
13 years 7 months ago
Arabic Named Entity Recognition: Using Features Extracted from Noisy Data
Building an accurate Named Entity Recognition (NER) system for languages with complex morphology is a challenging task. In this paper, we present research that explores the featur...
Yassine Benajiba, Imed Zitouni, Mona T. Diab, Paol...
ICASSP
2010
IEEE
13 years 10 months ago
Acoustic model adaptation via Linear Spline Interpolation for robust speech recognition
We recently proposed a new algorithm to perform acoustic model adaptation to noisy environments called Linear Spline Interpolation (LSI). In this method, the nonlinear relationshi...
Michael L. Seltzer, Alex Acero, Kaustubh Kalgaonka...
AIRS
2008
Springer
13 years 11 months ago
Efficient Feature Selection in the Presence of Outliers and Noises
Although regarded as one of the most successful algorithm to identify predictive features, Relief is quite vulnerable to outliers and noisy features. The recently proposed I-Relief...
Shuang-Hong Yang, Bao-Gang Hu