|
Langley P. Selection of relevant features in machine learning. In: Proc. AAAI Fall Symposium on Relevance, New Orleans, Louisiana, 1994: 140-144.[2]Gilad-Bachrach R.[J].Navot A, and Tishby N. Margin based feature selection - theory and algorithms. In proceedings of the 21st International Conference on Machine Learning (ICML), Banff, Alberta, Canada, July 4-.2004,:-[3]Crammer K, Gilad-Bachrach R, Navot A, and Tishby N. Margin analysis of the lvq algorithm. Proc.17th Conference on Neural Information Processing Systems, Banff, Alberta, Canada, 2002: 462-469.[4]Vapnik V. The Nature of Statistical Learning Theory. Spring- Verlag, 1995.[5]Freund Y and Schapire R E. A decision-theoretic generalization of on-line learning and an application to boosting[J].Journal of Computer and System Sciences.1997, 55(1):119-139[6]Buckingham L and Geva L. Lvq is a maximum margin algorithm. In Pacific Knowledge Acquisition Workshop PKAW2000, Sydney, Australia, 2000.[7]Weston J, Mukherjee S, Chapelle O, Pontil M, Poggio T, and Vapnik V. Feature selection for SVMs. In Advances in Neural Information Processing Systems. MIT Press, 2000, (13): 668-674.[8]Grandvalet Y and Canu S. Adaptive scaling for feature selection in svms. In Advances in Neural Information Processing Systems, MIT Press, 2003, (15): 553-560.[9]Guyon I, Weston J, Barnhill S, and Vapnik V. Gene selection for cancer classification using support vector machines[J].Machine Learning.2002, 46(1/3):389-422[10]Ahmad A and Dey L. A feature selection technique for classificatory analysis[J].Pattern Recognition Letters.2005, 26(1):43-56[11]Weihong L and Weiguo G. Feature selection based KPCA, SVM and GSFS for face recognition. Proceeding of 3rd International Conference on Advances in Pattern Recognition, London, 2005, 2: 344-350.[12]Bartlett P. The sample complexity of pattern classification with neural networks: the size of the weights is more important than the size of the network[J].IEEE Trans. on Information Theory.1998, 44(2):525-536[13]Kira K and Rendell L A. The feature selection problem: traditional methods and a new algorithm. In: Proc. of the Ninth National Conf. on Artificial Intelligence, Menlo Park, CA, USA, 1992: 129-134.[14]Nastar C and Ayache N. Frequency-based non-rigid motion analysis[J].IEEE Trans. on Pattern Anal. Machine Intell.1996, 18(11):1067-1079
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百度学术
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