DocumentCode
2286501
Title
Support vector machine for regression and applications to financial forecasting
Author
Trafalis, Theodore B. ; Ince, Huseyin
Author_Institution
Sch. of Ind. Eng., Oklahoma Univ., Norman, OK, USA
Volume
6
fYear
2000
fDate
2000
Firstpage
348
Abstract
The main purpose of the paper is to compare the support vector machine (SVM) developed by Cortes and Vapnik (1995) with other techniques such as backpropagation and radial basis function (RBF) networks for financial forecasting applications. The theory of the SVM algorithm is based on statistical learning theory. Training of SVMs leads to a quadratic programming (QP) problem. Preliminary computational results for stock price prediction are also presented
Keywords
backpropagation; finance; forecasting theory; function approximation; quadratic programming; radial basis function networks; financial forecasting; regression; statistical learning theory; stock price prediction; support vector machine; Backpropagation algorithms; Industrial engineering; Machine learning; Machine learning algorithms; Pattern classification; Quadratic programming; Statistical learning; Support vector machine classification; Support vector machines; Virtual colonoscopy;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on
Conference_Location
Como
ISSN
1098-7576
Print_ISBN
0-7695-0619-4
Type
conf
DOI
10.1109/IJCNN.2000.859420
Filename
859420
Link To Document