Titlebook: Maximum Entropy and Bayesian Methods; Santa Fe, New Mexico Kenneth M. Hanson,Richard N. Silver Conference proceedings 1996 Kluwer Academic

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Mixture Modeling to Incorporate Meaningful Constraints into Learning, this difficult problem has been elaborated by both symbolic machine learning and neural networks communities. However, no fairly general methodology has emerged yet. The contribution of this paper is two-folded. First, we propose a Bayesian view of domain knowledge incorporation. In our framework t

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Maximum Entropy (Maxent) Method in Expert Systems and Intelligent Control: New Possibilities and Liy, it is natural to use probabilities to describe uncertainty of the system’s answer to a given query Q. Since it is impossible to inquire about the expert’s probabilities for all possible (≥ 2.) propositional combinations of E.., a knowledge base is usually incomplete in the sense that there are ma

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mortgage

Continuum Models for Bayesian Image Matching,ential to the inference of the mapping because the image features on which matching is based are sparsely distributed and, consequently, underconstrain the problem. In this paper, we describe the Bayesian approach to image matching and introduce suitable priors based on idealized models of continua.

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