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Hybrid intelligent systems which take advantage of the Bayesian/Fuzzy collaboration have been explored in the literature in the last years. Such collaboration can play an important role mainly in real intelligent systems applications, where accuracy and comprehensibility are crucial aspects to be considered. This paper further explore the Bayes Fuzzy method proposing a classification method specially...
Bayesian networks (BNs) are probabilistic graphical models that are widely used for building diagnosis- and decision-support expert systems. The construction of BNs with the help of human experts is a difficult and time consuming task, which is prone to errors and omissions especially when the problems are very complicated. Learning the structure of a Bayesian network model and causal relations from...
The implementation of a NASA policy to collect R&M data to support ongoing and future projects has been limited, due in part to NASA's organization into largely independently operating programs and projects and the lack of a structured approach for the distribution of R&M data across those programs and projects. This paper describes the concept of a data collection and analysis environment...
Many reliability databases pool event data for equipment across different plants. Pooling may occur both within and between organizations with the intention of sharing data across common items within similar operating environments to provide better estimates of reliability and availability. Frequentist estimation methods can be poor when few, or no, events occur even when equipment operate for long...
We have investigated a technique for recognising faces invariant of facial expressions. We apply multi-linear tensor algebra, which subsumes linear algebra, to analyse and recognise 3D face surfaces. This potent framework possesses a remarkable ability to deal with the shortcomings of principle component analysis in less constrained situations. A set of vector spaces can be used to represent the variation...
A new hierarchical nonparametric Bayesian framework is proposed for the problem of multi-task learning (MTL) with sequential data. The models for multiple tasks, each characterized by sequential data, are learned jointly, and the intertask relationships are obtained simultaneously. This MTL setting is used to analyze and sort large databases composed of sequential data, such as music clips. Within...
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