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  • 标题:Classification of Real Value and Complex Value Data using Machine Learning Concepts
  • 本地全文:下载
  • 作者:S.Padma ; B.Kanchana Devi
  • 期刊名称:International Journal of Computer Science and Information Technologies
  • 电子版ISSN:0975-9646
  • 出版年度:2014
  • 卷号:5
  • 期号:2
  • 页码:2361-2364
  • 出版社:TechScience Publications
  • 摘要:Learning is acquiring knowledge which makes man to study new things. The familiarity of the new concept is attained by the machine by giving repeated training on the same concept. In this way machines can also learn by repeated training on the same set of data. Data mining includes the concept of classification, which can be done by machine learning algorithms. Data that are to be classified can also be complex values. The performance varies if both the real and imaginary part are considered for classification. There are different machine algorithms with different features. Some of the machine learning algorithms such as Support Vector Machines (SVM), Extreme Learning Machines (ELM), Self- Adaptive Resource Allocation Network (SRAN) and Phase Encoded Complex-Valued Extreme Learning Machine (PECELM )are considered. This paper gives a comparison of these algorithms with its working nature and discusses the simulated results performed by these algorithms on balanced and imbalanced dataset for complex values and real values
  • 关键词:(Machine Learning; Support Vector Machines;Extreme Learning Machines; Self-Adaptive Resource;Allocation Network; Phase Encoded Complex-Valued;Extreme Learning Machine.)
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