Data Mining: Foundations and Intelligent Paradigms
Volume 3: Medical, Health, Social, Biological and other Applications
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9 Result(s)
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Chapter
Introduction to Bayesian Networks
Reasoning with incomplete and unreliable information is a central characteristic of decision making, for example in industry, medicine and finance. Bayesian networks provide a theoretical framework for dealing...
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Chapter
Toward a Generalized Bayesian Network
The author’s past work in this area has shown that the probability of a state of a Bayesian network, found using the standard Bayesian techniques, could be equated to the Maximum Entropy solution and that this...
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Book
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Book
Data Mining: Foundations and Intelligent Paradigms
Volume 2: Statistical, Bayesian, Time Series and other Theoretical Aspects
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Book
Data Mining: Foundations and Intelligent Paradigms
Volume 1: Clustering, Association and Classification
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Chapter
Advanced Modelling Paradigms in Data Mining
As discussed in the previous volume, the term Data Mining grew from the relentless growth of techniques used to interrogation masses of data. As a myriad of databases emanated from disparate industries, enterpris...
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Chapter
Advances in Intelligent Data Mining
The human body is composed of eleven sub-systems. These include the: respiratory, digestive, muscular, immune, circulatory, digestive, skeletal, endocrine, urinary, integumentary and reproductive systems [1]. ...
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Chapter
Data Mining Techniques in Clustering, Association and Classification
The term Data Mining grew from the relentless growth of techniques used to interrogation masses of data. As a myriad of databases emanated from disparate industries, management insisted their information officers...
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Chapter
Bayesian Networks: Theory and Philosophy
This chapter explores the theory of Bayesian networks with particular reference to Maximum Entropy Formalism. A discussion of objective Bayesianism is given together with some brief remarks on applications.
