Neural Networks and Rules-based Systems used to Find Rational and Scientific Correlations between being Here and Now with Afterlife Conditions
Neural Networks and Rules-based Systems used to Find Rational and
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The development of data-mining applications such as classification and clustering has shown the need for machine learning algorithms to be applied to large scale data. The article gives an overview of some of the most popular machine learning methods (Gaussian and Nearest Mean) and of their applicability to the problem of spam e-mail filtering. The aim of this paper is to compare and investigate the effectiveness of classifiers for filtering spam e-mails using different matrices. Since spam is increasingly becoming difficult to detect, so these automated techniques will help in saving lot of time and resources required to handle e-mail messages.
Dr. Upasna Attri. 2012. \u201cComparative Study of Gaussian and Nearest Mean Classifiers for Filtering Spam E-mails\u201d. Global Journal of Computer Science and Technology - E: Network, Web & Security GJCST-E Volume 12 (GJCST Volume 12 Issue E11): .
Crossref Journal DOI 10.17406/gjcst
Print ISSN 0975-4350
e-ISSN 0975-4172
The methods for personal identification and authentication are no exception.
The methods for personal identification and authentication are no exception.
Total Score: 107
Country: India
Subject: Global Journal of Computer Science and Technology - E: Network, Web & Security
Authors: Dr. Upasna Attri, Harpreet Kaur (PhD/Dr. count: 1)
View Count (all-time): 239
Total Views (Real + Logic): 10418
Total Downloads (simulated): 2537
Publish Date: 2012 07, Tue
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Neural Networks and Rules-based Systems used to Find Rational and
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The development of data-mining applications such as classification and clustering has shown the need for machine learning algorithms to be applied to large scale data. The article gives an overview of some of the most popular machine learning methods (Gaussian and Nearest Mean) and of their applicability to the problem of spam e-mail filtering. The aim of this paper is to compare and investigate the effectiveness of classifiers for filtering spam e-mails using different matrices. Since spam is increasingly becoming difficult to detect, so these automated techniques will help in saving lot of time and resources required to handle e-mail messages.
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