Titlebook: Information, Uncertainty and Fusion; Bernadette Bouchon-Meunier,Ronald R. Yager,Lotfi A Book 2000 Springer Science+Business Media New York

一回合

發(fā)怨言

On neurofuzzy and fuzzy decision tree approaches multilayer feedforward neural networks and fuzzy decision trees based on top down induction. Neurofuzzy systems take advantage of fuzzy logic and of the learning and approximation capabilities of neural networks to build fuzzy rule bases, while preserving the massively parallel computing character.

燒瓶

A Fuzzy Extension to a Temporal Parsimonious Covering Theorysary and possible manifestations, where all temporal information is modeled by fuzzy sets. Similarly, the temporal information about when manifestations started and ended in a particular case is also modeled by fuzzy sets. The paper proposes different measures on how well a disorder explains the man

針葉類的樹

Probabilities of Future Decisionsstruct a Bayesian network that enables the decision maker to examine the optimal strategy, or any other strategy, with respect to other criteria than maximal expected utility. We introduce the concept: ‘the probability of making a future decision’, and show how the constructed Bayesian network can b

中止

Representational Issues for Risk Scenariosrisk is explored, together with the influence of alternative uncertainty representations on risk models and the choices open to risk managers when trying to represent risk for processing purposes. An attempt at a generic risk representation for complex scenarios is presented, together with a process

BALE

Zero probabilities in stochastic independence related to events of probability 0 or 1) has been introduced in that paper. We give here a new “improved” definition of independence of two events (coinciding with the former one when some relevant events have probabilities different from 0 and 1): it allows to deepen and clarify further aspects (i

Forehead-Lift

Computing the Probability of Formulas Representing Events in Product Spacesenting the set of diagnoses in the general theory of model-based diagnosis presented by Reiter [.]. In this paper it is shown that the mathematical structure of a Boolean algebra is the appropriate concept to define and describe the language. After having established some general results about Boole

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rods366

A Rule-based Language for Probabilistic Reasoningic. It is particularly suited for reasoning with belief networks as its semantics explicitly captures the inherent conditional independencies. One salient feature of the proposed language is that it can be extended to include functional symbols and recursion. Moreover, our language subsumes the trad