Architectures of Multi-strategy Learning for Distributed Intelligent Agents in Mobile-Commerce
Date Issued
2004-07-31
Date
2004-07-31
Author(s)
DOI
922213E002009
Abstract
In this project we investigated the architectures and applications of multistrategy
learning for distributed intelligent agents for mobile-commerce. It makes
use of multistrategy learning and hybrid knowledge base such that the intelligent
agents can adapt themselves to environmental changes via constructing the knowledge
base and the rule base. Support vector machines like other classification approaches
aim to learn the decision surface from the input points for classification
problems or regression problems. In many applications, each input points may be
associated with different weightings to reflect their relative strengths to conform to
the decision surface. In our previous research, we applied a fuzzy membership to each
input point and reformulate the support vector machines to be fuzzy support vector
machines (FSVMs) such that different input points can make different contributions
to the learning of the decision surface.
FSVMs provide a method for the classification problem with noises or outliers.
However, there is no general rule to determine the membership of each data point.
We can manually associate each data point with a fuzzy membership that can reflect
their relative degrees as meaningful data. To enable automatic setting of memberships,
we introduce two factors in training data points, the confident factor and the
trashy factor, and automatically generate fuzzy memberships of training data points
from a heuristic strategy by using these two factors and a mapping function. We
investigate and compare two strategies in the experiments and the results show that
the generalization error of FSVMs are comparable to other methods on benchmark
datasets.
Subjects
intelligent agents
support vector machines
fuzzy membership
fuzzy
SVM
SVM
noisy data traning
Publisher
臺北市：國立臺灣大學電機工程學系暨研究所
Type
report
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