By Thomas Diettrich, Suzanna Becker, Zoubin Ghahramani

ISBN-10: 0262042088

ISBN-13: 9780262042086

The once a year convention on Neural details Processing structures (NIPS) is the flagship convention on neural computation. The convention is interdisciplinary, with contributions in algorithms, studying concept, cognitive technology, neuroscience, imaginative and prescient, speech and sign processing, reinforcement studying and regulate, implementations, and various purposes. basically approximately 30 percentage of the papers submitted are accredited for presentation at NIPS, so the standard is phenomenally excessive. those lawsuits comprise the entire papers that have been offered on the 2001 convention.

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Additional info for Advances in Neural Information Processing Systems 14: Proceedings of the 2001 Conference

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E, on the choice of confidence levels or a-cuts induced by these confidence levels [9]. For instance, one expert assigns the same confidence degree to two possibilistic variables A and B, This suggests a strong dependency between the precision of A and the precision of B. However, this form of dependency does not suppose any objective dependence between possibilistic variables themselves. ,X„ are linked or not. The hybrid method also supposes independence between the group of probabilistic and possibilistic variables.

To underline the way of proportionalization we call this PT as a proportional belief transformation PropBT now. (^)--WIf E B E X ^ ( ^ ) — 0 then |X| is used instead of it, thus m{X) is reallocated per the same portions among all elements of X in such a case. Unfortunately, it is similarly to BeLT significantly sensitive to belief (i. e. to m{X)) of singletons. To improve it the stepwise proportional transformation has been suggested, where m{X) for \X\ — {n-\-l—i), are reallocated in i-th step among m{X) for \X\ = {n — i).

E. variability. As can be observed on some examples, the use of 7cJ may put excessive weight on outlier fuzzy values, in the case of precise results with high variability, even if some outliers have been deleted by the threshold d. The postprocessing proposed by Guyonnet et al. is thus debatable. Better alternative postprocessings can separately treat variability and imprecision. 2. Measuring variability and imprecision separately We may wis;h to define separate indices of variability and imprecision.

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Advances in Neural Information Processing Systems 14: Proceedings of the 2001 Conference by Thomas Diettrich, Suzanna Becker, Zoubin Ghahramani

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