SVM-based Cancer Incidence Forecasting of Patients

被引：0

作者：

Ai, Xu Xin ^{[1
]}

Jia, Hu ^{[1
]}

Xin, Lu ^{[1
]}

机构：

[1] Nan Chang Normal Univ, Dept Math & Comp Sci, Nanchang, Jiangxi, Peoples R China

来源：

PROCEEDINGS OF 2016 9TH INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE AND DESIGN (ISCID), VOL 2 | 2016年

关键词：

SVM; Kernel Function; Attribute Reduction; Data Preprocessing;

D O I：

10.1109/ISCID.2016.179

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

As one of the machine learning methods that has been widely used in recent years, SVM can be applied to pattern classification and nonlinear regression. This paper proposes the basic modeling process by using SVM, and introduces the processing technique of dimension reduction by using MATLAB and principal component analysis method, and provides the process of classification forecasting by using SVM taking a data set of cancer patients as an example. Experiments show that the method is simple, easy to use and effective.

引用

页码：281 / 284

页数：4

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