Ship Track Regression Based on Support Vector Machine

Abstract

This work was supported in part by the National Natural Science Foundation of China under Grant 61473331, in part by the Natural Science Foundation of Guangdong Province of China under Grant 2014A030307049, in part by the Ordinary University Innovation of Guangdong Province of China under Grant 2015KTSCX094, in part by the Sail Plan Training High-Level Talents of Guangdong Province of China, in part by the Science and Technology Plan of Guangdong Province of China under Grant 2015B020233019, in part by the High-level Personnel of Institutions of Higher Learning of Guangdong under Grant [2013] 246.152, in part by the Scientific Research Foundation of Discipline and Specialty Construction in Higher Education of Guangdong under Grant 2013KJCX0133, in part by the 2016 Annual Scientific and Technological Innovation Special Fund to foster Students Projects of Guangdong under Grant pdjh2016b0341, in part by the Guangdong University of Petrochemical Technology College Students' Innovation Incubation Project under Grant 2015pyA006, and in part by the Science and Technology Project of Guangzhou under Grant 201604010099, Grant 2016B030306002, and Grant 2016B030308001.

Keywords

• Ship track regression
• ship maneuvering motion
• nonlinear regression
• support vector machine (SVM)