Kernel Adatron Algorithm of Support Vector Machine for Function Approximation


The Transactions of the Korea Information Processing Society (1994 ~ 2000), Vol. 7, No. 6, pp. 1867-1873, Jun. 2000
10.3745/KIPSTE.2000.7.6.1867,   PDF Download:

Abstract

Function approximation from a set of input-output pairs has numerous applications in scientific and engineering areas. Support vector machine (SVM) is a new and very promising classification, regression and function approximation technique developed by Vapnik and his group at AT&TG Bell Laboratories. However, it has failed to establish itself as common machine learning tool. This is partly due to the fact that this is not easy to implement, and its standard implementation requires the use of optimization package for quadratic programming (QP). In this appear we present simple iterative Kernel Adatron (KA) algorithm for function approximation and compare it with standard SVM algorithm using QP.


Statistics
Show / Hide Statistics

Statistics (Cumulative Counts from September 1st, 2017)
Multiple requests among the same browser session are counted as one view.
If you mouse over a chart, the values of data points will be shown.


Cite this article
[IEEE Style]
K. H. Seok and C. H. Hwang, "Kernel Adatron Algorithm of Support Vector Machine for Function Approximation," The Transactions of the Korea Information Processing Society (1994 ~ 2000), vol. 7, no. 6, pp. 1867-1873, 2000. DOI: 10.3745/KIPSTE.2000.7.6.1867.

[ACM Style]
Kyung Ha Seok and Chang Ha Hwang. 2000. Kernel Adatron Algorithm of Support Vector Machine for Function Approximation. The Transactions of the Korea Information Processing Society (1994 ~ 2000), 7, 6, (2000), 1867-1873. DOI: 10.3745/KIPSTE.2000.7.6.1867.