title: Fast modular network implementation for support vector machines.
abstract: Support vector machines (SVMs) have been extensively used. However, it is known that SVMs face difficulty in solving large complex problems due to the intensive computation involved in their training algorithms, which are at least quadratic with respect to the number of training examples. This paper proposes a new, simple, and efficient network architecture which consists of several SVMs each trained on a small subregion of the whole data sampling space and the same number of simple neural quantizer modules which inhibit the outputs of all the remote SVMs and only allow a single local SVM to fire (produce actual output) at any time. In principle, this region-computing based modular network method can significantly reduce the learning time of SVM algorithms without sacrificing much generalization performance. The experiments on a few real large complex benchmark problems demonstrate that our method can be significantly faster than single SVMs without losing much generalization performance.
authors.affiliation: School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore. gbhuang@ieee.org
authors.forename: Guang-Bin
authors.initials: GB
authors.lastname: Huang
authors.forename: K Z
authors.initials: KZ
authors.lastname: Mao
authors.forename: Chee-Kheong
authors.initials: CK
authors.lastname: Siew
authors.forename: De-Shuang
authors.initials: DS
authors.lastname: Huang
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*