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Smoothness and sparsity tuning for Semi-Supervised SVM
Gilles Gasso, Karina Zapien, Stephane Canu
85 - 86
10.3233/978-1-58603-898-4-85
NATO Science for Peace and Security Series - D: Information and Communication Security
Volume 19: Mining Massive Data Sets for Security
Abstract

Using unlabeled data to unravel the structure of the data to leverage the learning process is the goal of semi supervised learning. Kernel framework allows to model the data structure using graphs and to build kernel machines such as Laplacian SVM [1]. But a remark is the lack of sparsity in variables of the obtained model leading to a long running time for classification of new points. We provide a way of alleviating this problem by using a L1 penalty and a algorithm to efficiently compute the solution. Empirical evidence shows the benefit of the algorithm.

$35.00 / €27.50 / £22.00
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