title: Deep learning of support vector machines with class probability output networks.
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.
authors.affiliation: School of Electronics Engineering, Kyungpook National University, 1370 Sankyuk-Dong, Puk-Gu, Taegu 702-701, Republic of Korea.
authors.forename: Sangwook
authors.initials: S
authors.lastname: Kim
authors.affiliation: School of Electronics Engineering, Kyungpook National University, 1370 Sankyuk-Dong, Puk-Gu, Taegu 702-701, Republic of Korea.
authors.forename: Zhibin
authors.initials: Z
authors.lastname: Yu
authors.affiliation: College of Information and Communication Engineering, Sungkyunkwan University, 2066, Seobu-ro, Jangan-gu, Suwon, Gyeonggi-do 440-746, Republic of Korea. Electronic address: rmkil@skku.edu.
authors.forename: Rhee Man
authors.initials: RM
authors.lastname: Kil
authors.affiliation: School of Electronics Engineering, Kyungpook National University, 1370 Sankyuk-Dong, Puk-Gu, Taegu 702-701, Republic of Korea. Electronic address: mholee@knu.ac.kr.
authors.forename: Minho
authors.initials: M
authors.lastname: Lee
mesh_terms: D001499:Bayes Theorem; D010363:Pattern Recognition, Automated / Q000379:methods*; D060388:Support Vector Machine
keywords: Class probability output network; Deep learning; Support vector machine; Uncertainty measure