Learning and decision-making in the framework of fuzzy lattices
New learning paradigms in soft computing
One-class document classification via Neural Networks
Neurocomputing
A model for the integration of prioritized knowledge bases through subjective belief games
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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BI'11 Proceedings of the 2011 international conference on Brain informatics
Polarization and Non-Positive Social Influence: A Hopfield Model of Emergent Structure
International Journal of Knowledge and Systems Science
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Reasoning with uncertainty is a field with many different approaches and viewpoints, with important applications to sensor design and autonomous system development. It is important to have calculi for propagating measures of “probability” or “likelihood” even in cases of subjective information, and it is just as important to be able to propagate the “certitude” of this information. By choosing the semantics properly, this information can be handled by keeping track of certain statistics on a different probability space, (which we call the opinion space). The semantics assume that the “likelihood” or “probability numbers” are in fact averages over many (perhaps subjective) opinions and that uncertainty is represented by the spread in these opinions, which can be technically maintained by a covariance matrix. Different calculi result from different design choices consistent with this choice of semantics. It also turns out that certain mechanisms that are frequently considered “non-Bayesian”, result from specific choices for representing the statistics and dependency assumptions