A Comparative study of Classification techniques: Support vector Machine, Fuzzy Support vector Machine & Decision Trees

被引：0
|
作者：
Pandey, Priyank [1 ]
Jain, Amita [1 ]
机构：
[1] Guru Gobind Singh Indraprastha Univ, AIACTR, Dept Comp Sci & Engn, Delhi, India
来源：
PROCEEDINGS OF THE 10TH INDIACOM - 2016 3RD INTERNATIONAL CONFERENCE ON COMPUTING FOR SUSTAINABLE GLOBAL DEVELOPMENT | 2016年
关键词：
classification; decision trees; FSVM; hyper plane; SVM;
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
A support vector machine (SVM) is a classification technique that learns the decision surface from two different classes of the input points. There are many cases where these input points cannot be assigned to one specific class. The fuzzy support vector machine (FSVM) was proposed for this purpose. One another classification technique is decision tree. In this paper we are doing a comparative study of these three classification methods. We are doing comparison in terms of inputs and the produced output.
引用
收藏
页码：3620 / 3624
页数：5
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