• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.731-739
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• 1998

The probabilistic relaxation(PR) scheme based on the conditional probability and probability space partition has the important property that when its compatibility coefficient matrix (CCM) has uniform components it can classify m-dimensional probabilistic distribution vectors into different classes. When consistency or inconsistency measures have been defined, the properties of PRs are completely determined by the compatibility coefficients among labels of labeled objects and influence weight among labeled objects. In this paper we study the properties of PR in which both compatibility coefficients and influence weights are uniform, and then a learning rule for such PR system is derived. Experiments have been performed to verify the effectiveness of the learning rule.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1998.06a
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• pp.413-416
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• 1998

In this paper we first show that uniform PR systems and half independent PR systems have same dynamics, and then an important property of this two kinds of systems is derived. The most important property of uniform PR systems is that they have the ability of classifying m-dimensional problabilistic vector into in classes. The significance of studying the dynamics of uniform PR systems are tried from the beginning with a uniform PR system.