TY - JOUR
T1 - Multiple Classification Using SVM Based Multi Knowledge Based System
AU - Ramanathan, Thirumalaimuthu Thirumalaiappan
AU - Sharma, Dharmendra
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
KW - knowledge-based system
KW - Support vector machine
UR - http://www.scopus.com/inward/record.url?scp=85032436715&partnerID=8YFLogxK
U2 - 10.1016/j.procs.2017.09.139
DO - 10.1016/j.procs.2017.09.139
M3 - Article
AN - SCOPUS:85032436715
SN - 1877-0509
VL - 115
SP - 307
EP - 311
JO - Procedia Computer Science
JF - Procedia Computer Science
T2 - 7th International Conference on Advances in Computing and Communications, ICACC 2017
Y2 - 22 August 2017 through 24 August 2017
ER -