子計畫一：行動電子商務中多策略學習之分散式智慧型代理 人架構(2/3)

Learning algorithms are the basic part of intelligent agent. Different learning
algorithms can affect the performance and behavior of the intelligent agent.
Classification algorithms used in many intelligent agent applications are one of these
learning algorithms. Intelligent agents can learn the behavior from known data and
predict or behave from future data by classification algorithms. Fuzzy support vector
machines (FSVMs) provide a way to classify data with noises or outliers. Each data
point is associated with a fuzzy membership that can reflect their relative degrees as
meaningful data. In this report, we investigate and compare two strategies of
automatically setting the fuzzy memberships of data points. It makes the usage of
FSVMs easier in the application of reducing the effects of noises or outliers such that
makes better behavior of intelligent agents. The experiments show that the
generalization error of FSVMs is comparable to other methods on benchmark
datasets.