Sparse Support Vector Machines
نویسنده
چکیده
Support Vector Machines (SVMs) are state-of-the-art algorithms for classification in machine learning. However, the SVM formulation does not directly seek to find sparse solutions. In this work, we propose an alternate formulation that explicitly imposes sparsity. We show that the proposed technique is related to the standard SVM formulation and therefore shares similar theoretical guarantees. We further show that proposed formulation performs comparable to the standard SVM formulation on several synthetic datasets.
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