title: Statistical properties of support vector machines with forgetting factor.
abstract: Introducing a forgetting factor allows a support vector machine to solve time-varying problems adaptively. However, the exponential forgetting factor proposed in an earlier work does not ensure convergence of average generalization error even for a simple linearly separable problem. To guarantee convergence, we propose a factorial forgetting factor which decays factorially over time. We approximately derive the average generalization error of the factorial forgetting factor as well as that of the exponential forgetting factor using a simple one-dimensional problem, and confirm our theory by computer simulations. Finally, we show that our theory can be extended to arbitrary types of forgetting factors for simple linearly separable cases.
authors.affiliation: Graduate School of Information Science, Nara institute of Science and Technology, 630-0101, Japan. funaya@gmail.com
authors.forename: Hiroyuki
authors.initials: H
authors.lastname: Funaya
authors.forename: Kazushi
authors.initials: K
authors.lastname: Ikeda
mesh_terms: D001185:Artificial Intelligence; D003198:Computer Simulation; D016571:Neural Networks, Computer; D011340:Problem Solving; D060388:Support Vector Machine