L2 Support Vector Machines Revisited - Novel Direct Learning Algorithm and Some Geometric Insights
نویسندگان
چکیده
The paper presents a novel learning algorithm for the class of L2 Support Vector Machines classifiers dubbed Direct L2 SVM. The proposed algorithm avoids solving the quadratic programming problem and yet, it produces both the same exact results as the classic quadratic programming based solution in a significantly shorter CPU time. The connections between various L2 SVM algorithms will be highlighted and some geometric properties of the Direct L2 SVM will be pointed at. All the other known L2 based SVMs can be looked at as the special cases of a Direct L2 SVM. The developed Direct L2 SVM algorithm is posed as the Non-Negative Least Squares problem which solves the comprehensive L2 SVM exactly and, in a striking difference to both the Least Squares SVM and proximal SVM, is able to produce the sparse solutions.
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