Feature selection for regularized least-squares: New computational short-cuts and fast algorithmic implementations
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[1] T. Salakoski,et al. Learning to Rank with Pairwise Regularized Least-Squares , 2007 .
[2] Tapio Pahikkala,et al. An efficient algorithm for learning to rank from preference graphs , 2009, Machine Learning.
[3] Tong Zhang,et al. Adaptive Forward-Backward Greedy Algorithm for Sparse Learning with Linear Models , 2008, NIPS.
[4] Jing Peng,et al. SVM vs regularized least squares classification , 2004, ICPR 2004.
[5] Josef Kittler,et al. Floating search methods in feature selection , 1994, Pattern Recognit. Lett..
[6] Tapio Salakoski,et al. Fast n-Fold Cross-Validation for Regularized Least-Squares , 2006 .
[7] T. Salakoski,et al. Linear Time Feature Selection for Regularized Least-Squares , 2010, 1003.3570.
[8] Johan A. K. Suykens,et al. Low rank updated LS-SVM classifiers for fast variable selection , 2008, Neural Networks.
[9] Ron Kohavi,et al. Wrappers for Feature Subset Selection , 1997, Artif. Intell..
[10] Xin Yao,et al. Gene selection algorithms for microarray data based on least squares support vector machine , 2006, BMC Bioinformatics.
[11] Alexander Gammerman,et al. Ridge Regression Learning Algorithm in Dual Variables , 1998, ICML.
[12] Isabelle Guyon,et al. An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..
[13] R. Rifkin,et al. Notes on Regularized Least Squares , 2007 .
[14] Marc M. Van Hulle,et al. Speeding Up the Wrapper Feature Subset Selection in Regression by Mutual Information Relevance and Redundancy Analysis , 2006, ICANN.
[15] Ron Kohavi,et al. Irrelevant Features and the Subset Selection Problem , 1994, ICML.
[16] Arthur E. Hoerl,et al. Ridge Regression: Biased Estimation for Nonorthogonal Problems , 2000, Technometrics.