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普林斯顿大学贡三元教授讲座:A Robust Regression Approach to Machine Learning

 

Abstract: Regression analysis has been a major theoretical pillar for supervised machine learning as it is applicable to various identification and classification problems. Aiming at robust regressors, two major approaches have been adopted. The first category contains a variety of regularization techniques whose principle lies in incorporating both the error and penalty terms into the cost function. It is represented by the ridge regressor.   Other prominent examples include RBF approximation networks by Poggio and Girosi [1990] and Least-Squares SVM introduced by Suykens and Vandewalle [1999].

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