Support vector machine approach for fast classification

被引：0
|
作者：
Kianmehr, Keivan [1 ]
Alhajj, Reda
机构：
[1] Univ Calgary, Dept Comp Sci, Calgary, AB T2N 1N4, Canada
[2] Global Univ, Dept Comp Sci, Beirut, Lebanon
来源：
DATA WAREHOUSING AND KNOWLEDGE DISCOVERY, PROCEEDINGS | 2006年 / 4081卷
关键词：
classification; association rules; support vector machines; classification rules; data mining; machine learning;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this study, we propose a new technique to integrate support vector machine and association rule mining in order to implement a fast and efficient classification algorithm that overcomes the drawbacks of machine learning and association rule-based classification algorithms. The reported test results demonstrate the applicability, efficiency and effectiveness of the proposed approach.
引用
收藏
页码：534 / 543
页数：10
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