基于朴素贝叶斯核的支持向量机数据分类算法分析

J. Simangunsong, M. Zarlis, Tulus
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引用次数: 1

摘要

本文研究了支持向量机和朴素贝叶斯在数据挖掘中的应用。许多研究人员开展和开发了提高数据准确性和分类的方法,并取得了良好的效果。这项研究是通过对花的种类进行实验来进行的。在本研究中,结论是Naïve贝叶斯的性能优于支持向量机,Naïve贝叶斯有很好的结果,承诺帮助分类最佳值,以获得数据分组。此研究优于SVM。训练过程与准确率的差异为28%,测试过程与准确率的差异为0.83%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Analysis of Algorithms Support Vector Machine with Naive Bayes Kernel in Data Classification
This research is about SVM and Naive Bayes in data mining. Many researchers carry out and develop methods to improve the accuracy and classification of data in good results. This research was carried out by conducting experiments on the types of flowers. In this study, it was concluded that the performance of Naïve Bayes was better than Support Vector Machine, Naïve Bayes had excellent results that promised to help classify the best values to get data grouping. This research is better than SVM. The training process has a difference of 28% and a testing process of 0.83% with the accuracy.
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