TY - GEN
T1 - A robust SVM design for multi-class classification
AU - Cho, Minkook
AU - Park, Hyeyoung
PY - 2005
Y1 - 2005
N2 - When we apply support vector machines (SVM) to multiclass classification, some methods of combining the results of independent SVM for each class haven been used, However, the conventional methods may deteriorates generalization performance when the number of data in each class is small. To solve this problem, we proposed a new method, which uses only one SVM and train it to find some similarity measure between data samples. Through an experiment using real data, we confirm that the proposed method can give better classification performance than the conventional one.
AB - When we apply support vector machines (SVM) to multiclass classification, some methods of combining the results of independent SVM for each class haven been used, However, the conventional methods may deteriorates generalization performance when the number of data in each class is small. To solve this problem, we proposed a new method, which uses only one SVM and train it to find some similarity measure between data samples. Through an experiment using real data, we confirm that the proposed method can give better classification performance than the conventional one.
UR - http://www.scopus.com/inward/record.url?scp=33745603488&partnerID=8YFLogxK
U2 - 10.1007/11589990_199
DO - 10.1007/11589990_199
M3 - Conference contribution
AN - SCOPUS:33745603488
SN - 3540304622
SN - 9783540304623
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 1335
EP - 1338
BT - AI 2005
T2 - 18th Australian Joint Conference on Artificial Intelligence, AI 2005: Advances in Artificial Intelligence
Y2 - 5 December 2005 through 9 December 2005
ER -