f) 2019-Scopus Open Access (PDF)

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Subject: search.filters.subject.Support Vector Machine

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    WEB DATA CLASSIFICATION USING SUPPORT VECTOR MACHINE BACK PROPAGATION NEURAL NETWORK
    (Blue Eyes Intelligence Engineering and Sciences Publication, 2019-09) Arunpriya, C
    These days, the development of World Wide Web has surpassed a lot with extra desires. Extraordinary arrangement of content reports, transmission records and pictures were reachable inside the web it’s as yet expanding in its structures. Information handling is that the style of removing information’s realistic inside the web. Web mining could be a piece of information preparing that identifies with differed examination networks like data recovery, bearing frameworks and artificial insight. The data’s in these structures are very much organized from the beginning. This web mining receives a great deal of the date mining procedures to discover most likely supportive data from web substance. The ideas of web mining with its classifications were examined. The paper chiefly focused on the web Content mining undertakings along the edge of its procedures and calculations. In this paper we proposed AI calculation based order .SVM_BPM calculation grouped the web content information and thought about existing calculations our proposed arrangement calculation is high effective and less time calculation.
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    LINEAR KERNEL WITH WEIGHTED LEAST SQUARE REGRESSION CO-EFFICIENT FOR SVM BASED TAMIL WRITER IDENTIFICATION
    (Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP), 2019-07) Thendral, Tharmalingam; Vijaya, Vijayakumar
    Tamil writer identification is the task of identifying writer based on their Tamil handwriting. Our earlier work of this research based on SVM implementation with linear, polynomial and RBF kernel showed that linear kernel attains very low accuracy compared to other two kernels. But the observation shows that linear kernel performs faster than the other kernels and also it shows very less computational complexity. Hence, a modified linear kernel is proposed to enrich the performance of the linear kernel in recognizing the Tamil writer. Weighted least square parameter estimation method is used to estimate the weights for the dot products of the linear kernel. SVM implementation with modified linear kernel is carried out on different text images of handwriting at character, word and paragraph levels. Comparing the performance with linear kernel, the modified kernel with weighted least square parameter reported promising results.