Deep learning of support vector machines with class probability output networks. (April 2015)

• Record Type: Journal Article
• Authors: Kim, Sangwook; Yu, Zhibin; Kil, Rhee Man; Lee, Minho
• Is Part Of: Neural networks. Volume 64(2015:Apr.)
• Page Start: 19
• Page End: 28
• Publication Date: 2015-04
• Subjects: Deep learning -- Support vector machine -- Class probability output network -- Uncertainty measure; Neural computers -- Periodicals; Neural networks (Computer science) -- Periodicals; Neural networks (Neurobiology) -- Periodicals; Nervous System -- Periodicals; Ordinateurs neuronaux -- Périodiques; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux neuronaux (Neurobiologie) -- Périodiques; Neural computers; Neural networks (Computer science); Neural networks (Neurobiology); Periodicals; 006.32
• DOI: 10.1016/j.neunet.2014.09.007
• Languages: English
• ISSNs: 0893-6080

Abstract

Abstract: Deep learning methods endeavor to learn features automatically at multiple levels and allow systems to learn complex functions mapping from the input space to the output space for the given data. The ability to learn powerful features automatically is increasingly important as the volume of data and range of applications of machine learning methods continues to grow. This paper proposes a new deep architecture that uses support vector machines (SVMs) with class probability output networks (CPONs) to provide better generalization power for pattern classification problems. As a result, deep features are extracted without additional feature engineering steps, using multiple layers of the SVM classifiers with CPONs. The proposed structure closely approaches the ideal Bayes classifier as the number of layers increases. Using a simulation of classification problems, the effectiveness of the proposed method is demonstrated.