Deep Network with Support Vector Machines

Abstract

Deep learning methods aims at learning features automatically at multiple levels that allow the system to learn complex functions mapping the input to the output directly from data. This ability to automatically learn powerful features will become increasingly important as the amount of data and range of applications to machine learning methods continues to grow. In this context we propose a deep architecture model using Support Vector Machine (SVM) which has inherent ability to select data points important for classification with good generalization capabilities. Since SVM can effectively discriminate features, we used support vectors with kernel as non-linear discriminant features for classification. By stacking SVMs in to multiple layers, we can obtain deep features without extra feature engineering steps and get robust recognition accuracy. Experimental results show that the proposed method improves generalization performance on Wisconsin Breast Cancer dataset.