Study on Quasi-Linear Support Vector Machine and Its Applications

2016 Fiscal Year Final Research Report

• Project/Area Number: 25420452
• Research Category: Grant-in-Aid for Scientific Research (C)
• Allocation Type: Multi-year Fund
• Section: 一般
• Research Field: Control engineering/System engineering
• Research Institution: Waseda University
• Principal Investigator: FURUZUKI TAKAYUKI(HU, JINGLU) 早稲田大学, 理工学術院(情報生産システム研究科・センター), 教授 (50294905)
• Project Period (FY): 2013-04-01 – 2017-03-31
• Keywords: サポートベクターマシン / ニューラルネットワーク / 機械学習 / 適応制御 / パターン認識

Outline of Final Research Achievements

In this research, a quasi-linear support vector machine (SVM) is proposed. The quasi-linear SVM, on one hand, can be seen as a nonlinear SVR model with easy-to-use structure; on the other hand, it is a nonlinear SVM with data-dependent kernel, which can composed by using machine learning methods, kernel learning methods and even deep kernel learning methods. The quasi-linear SVM is applied to switching adaptive control and high-performance pattern recognition.

Free Research Field

工学・電気電子工学・制御・システム工学