Title :
Online modeling based on support vector machine
Author_Institution :
Sch. of Comput. Technol. & Autom., Tianjin Polytech. Univ., Tianjin, China
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;
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
DOI :
10.1109/CCDC.2009.5192324
