Multiple Classification Using SVM Based Multi Knowledge Based System

Thirumalaimuthu Thirumalaiappan Ramanathan, Dharmendra Sharma

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Support Vector Machine (SVM) is a machine learning classification technique that supports binary classification. In the recent years, efforts are made to extend the SVM algorithm to support multiple classifications. This paper presents a SVM based multi-knowledge-based system (SMK) design that supports multiple classifications. The proposed design is successfully tested on a classification problem. The benchmark car evaluation dataset from UCI machine learning repository is used for training and testing the SMK. The SMK shows good performance on this classification and shows good promise for the future.

Original languageEnglish
Pages (from-to)307-311
Number of pages5
JournalProcedia Computer Science
Volume115
DOIs
Publication statusPublished - 2017
Event7th International Conference on Advances in Computing and Communications, ICACC 2017 - Kochi, Kerala, India
Duration: 22 Aug 201724 Aug 2017

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Knowledge based systems
Support vector machines
Learning systems
Railroad cars
Testing

Cite this

Ramanathan, Thirumalaimuthu Thirumalaiappan ; Sharma, Dharmendra. / Multiple Classification Using SVM Based Multi Knowledge Based System. In: Procedia Computer Science. 2017 ; Vol. 115. pp. 307-311.
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Multiple Classification Using SVM Based Multi Knowledge Based System. / Ramanathan, Thirumalaimuthu Thirumalaiappan; Sharma, Dharmendra.

In: Procedia Computer Science, Vol. 115, 2017, p. 307-311.

Research output: Contribution to journalArticle

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