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首页> 外文期刊>International Journal of Information Technology and Computer Science >Analysis of Parametric & Non Parametric Classifiers for Classification Technique using WEKA
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Analysis of Parametric & Non Parametric Classifiers for Classification Technique using WEKA

机译:基于WEKA的分类技术的参数和非参数分类器分析。

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摘要

In the field of Machine learning & Data Mining, lot of work had been done to construct new classification techniques/ classifiers and lot of research is going on to construct further new classifiers with the help of nature inspired technique such as Genetic Algorithm, Ant Colony Optimization, Bee Colony Optimization, Neural Network, Particle Swarm Optimization etc. Many researchers provided comparative study/ analysis of classification techniques. But this paper deals with another form of analysis of classification techniques i.e. parametric and non parametric classifiers analysis. This paper identifies parametric & non parametric classifiers that are used in classification process and provides tree representation of these classifiers. For the analysis purpose, four classifiers are used in which two of them are parametric and rest of are non-parametric in nature.
机译:在机器学习和数据挖掘领域,已经做了很多工作来构建新的分类技术/分类器,并且正在借助遗传算法,蚁群优化等受自然启发的技术进行更多的研究来构建新的分类器。 ,蜂群优化,神经网络,粒子群优化等。许多研究人员提供了分类技术的比较研究/分析。但是本文涉及分类技术的另一种分析形式,即参数和非参数分类器分析。本文确定了在分类过程中使用的参数和非参数分类器,并提供了这些分类器的树表示形式。出于分析目的,使用了四个分类器,其中两个分类器是参数化的,其余分类器本质上是非参数化的。

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