Support vector machine for regression and applications to financial forecasting

被引：136

作者：

Trafalis, TB ^{[1
]}

Ince, H ^{[1
]}

机构：

[1] Univ Oklahoma, Sch Ind Engn, Norman, OK 73019 USA

来源：

IJCNN 2000: PROCEEDINGS OF THE IEEE-INNS-ENNS INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOL VI | 2000年

关键词：

D O I：

10.1109/IJCNN.2000.859420

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The main purpose of this paper is to compare the support vector machine (SVM) developed by Vapnik 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.

引用

页码：348 / 353

页数：6

共 14 条

[1]

ANCONA N, 0299 RIIESICNR

[2]

[Anonymous], [No title captured], DOI DOI 10.1023/A:1009715923555

[3]

[Anonymous], PROPERTIES SUPPORT V

[4]

Bazaraa M.S., 2013, Nonlinear Programming-Theory and Algorithms, V3rd

[5]

CORTES C, 1995, MACH LEARN, V20, P273, DOI 10.1023/A:1022627411411

[6]

Haykin S., 1999, Neural Networks: A Comprehensive Foundation, V2nd ed

[7]

JOACHIMS T, 1998, LS824 U DORTM COMP S

[8]

OSUNA EE, 1997, 1602 AI MIT ART INT

[9]

SCHOLKOPF B, SHRINKING TUBE NEW S

[10]

Smola A. J., 1998, NEUROCOLT2 TECHNICAL

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