Online modeling based on support vector machine

Author

Wang, Shuzhou

Author_Institution

Sch. of Comput. Technol. & Autom., Tianjin Polytech. Univ., Tianjin, China

fYear

2009

fDate

17-19 June 2009

Firstpage

1188

Lastpage

1191

Abstract

Support vector machine (SVM) is a new method based on statistical learning theory. Online algorithms for training SVM are efficient to run, easy to implement comparing with batch algorithms. Presently online algorithms usually do not provide with the ability to explicitly control the number of support vectors. A modified online algorithm for SVM is proposed, witch has a budget parameter to explicitly control the number of support vectors. The proposed algorithm was applied to construct intelligent model of helicopter. It is shown by simulation that the modified online algorithm can reduce the number of support vectors effectively with similar generalization ability.

Keywords

statistical analysis; support vector machines; SVM; online algorithms; online modeling; statistical learning theory; support vector machine; Automation; Dictionaries; Helicopters; Linear approximation; Machine intelligence; Numerical stability; Statistical learning; Support vector machine classification; Support vector machines; Training data; Helicopter; Online Algorithms; Simulation Model; Support Vector Machine;

fLanguage

English

Publisher

ieee

Conference_Titel

Control and Decision Conference, 2009. CCDC '09. Chinese

Conference_Location

Guilin

Print_ISBN

978-1-4244-2722-2

Electronic_ISBN

978-1-4244-2723-9

Type

conf

DOI

10.1109/CCDC.2009.5192324

Filename

5192324