A benchmark study regarding Extreme Learning Machine, modified versions of Naive Bayes Classifier and Fast Support Vector Classifier

被引：0
|
作者：
Enache, Marinel [1 ]
Dogaru, Radu [2 ]
机构：
[1] Univ Politehn Bucuresti, Doctoral Sch Elect Telecommun & Informat Technol, Bucharest, Romania
[2] Univ Politehn Bucuresti, Nat Comp Lab, Dept Appl Elect & Informat Engn, Bucharest, Romania
来源：
2015 E-HEALTH AND BIOENGINEERING CONFERENCE (EHB) | 2015年
关键词：
artificial intelligence; neural networks; machine learning; universal approximation; radial basis function;
D O I：
暂无
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
This paper aims to highlight the performances and advantages of three improved and fast AI algorithms that are mainly used in classification problems suitable for various fields. The discussions regarding the benchmark results appeal to the Modified version of Radial Basis Function (RBF-M) mentioned in the paper as Fast Support Vector Classifier (FSVC) or Fast Support Vector Machine, Extreme Learning Machine (ELM) with its randomness model and a reduced complexity version for Naive Bayes (NB) algorithm. The performance studies conducted shows a good capacity of these networks to be used in medical embedded systems.
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页数：4
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