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Publication Type : Conference Paper
Thematic Areas : Center for Computational Engineering and Networking (CEN)
Publisher : 2015 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2015
Source : 2015 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2015, art. no. 7275837, pp. 1571-1577
Campus : Coimbatore
School : School of Engineering
Center : Computational Engineering and Networking
Department : Center for Computational Engineering and Networking (CEN)
Year : 2015
Abstract : Along the prompt growth in World Wide Web, the availability and accessibility of regional language contents such as e-books, web pages, e-mails, and digital repositories has grown exponentially. As a result, the automatic document classification has become the hotspot for fetching information among the millions of web documents. The idea of classifying the text, forms the baseline for many NLP applications such as information extraction, query response, information summarization, etc. The main objective of this paper is to develop an computational framework for supervised Tamil document classification task. This paper highlights the performance of Random Kitchen Sink, a randomization algorithm, in Grand Unified Regularized Least Squares (GURLS), a Machine Learning Library, is proven to be comparably better than the conventional kernel based classifier in terms of accuracy. Henceforth, we claim that Random Kitchen Sink can be an effective alternative to the kernels for a classifier. © 2015 IEEE.
Cite this Research Publication : Sanjanasri, J.P., Anand Kumar, M. A computational framework for Tamil document classification using Random Kitchen Sink, 2015 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2015, art. no. 7275837, pp. 1571-1577