TY - GEN
T1 - Two-dimensional concept vector machines based on an ionic interaction model
AU - Kim, Hyunsoo
AU - Park, Haesun
PY - 2005
Y1 - 2005
N2 - Support vector machines (SVMs) have shown state-of-the-art performance for many applications. However, SVMs suffer from high computational complexity for classifying new data points when the number of support vectors is large, since the decision boundary is represented as a linear combination of the support vectors. In this paper, we propose a two-dimensional concept vector machine based on an ionic interaction model. It generates a simple decision boundary by a small number of concept vectors. We compared its performance with SVMs.
AB - Support vector machines (SVMs) have shown state-of-the-art performance for many applications. However, SVMs suffer from high computational complexity for classifying new data points when the number of support vectors is large, since the decision boundary is represented as a linear combination of the support vectors. In this paper, we propose a two-dimensional concept vector machine based on an ionic interaction model. It generates a simple decision boundary by a small number of concept vectors. We compared its performance with SVMs.
UR - http://www.scopus.com/inward/record.url?scp=33847094573&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33847094573&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:33847094573
SN - 0780394224
SN - 9780780394223
T3 - Proceedings of 2005 International Conference on Neural Networks and Brain Proceedings, ICNNB'05
SP - 1991
EP - 1995
BT - Proceedings of 2005 International Conference on Neural Networks and Brain Proceedings, ICNNB'05
T2 - 2005 International Conference on Neural Networks and Brain Proceedings, ICNNB'05
Y2 - 13 October 2005 through 15 October 2005
ER -