Titlebook: Algorithmic Decision Theory; First International Francesca Rossi,Alexis Tsoukias Conference proceedings 2009 Springer-Verlag Berlin Heidel

周年紀(jì)念日

0302-9743 e papers range from computational social choice preference modeling, from uncertainty to preference learning, from multi-criteria decision making to game theory.978-3-642-04427-4978-3-642-04428-1Series ISSN 0302-9743 Series E-ISSN 1611-3349

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International Political Economy Seriesme .?

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反省

Conference proceedings 2009an Servolo, a small island of the Venice lagoon, during October 20-23, 2009. The program of the conference included oral presentations, posters, invited talks, and tutorials. The conference received 65 submissions of which 39 papers were accepted (9 papers were posters). The topics of these papers r

Chronological

Dependency or Interdependency in Old Agecy improvement and deterioration due to noise emerge only at higher noise levels with manipulation than without it. In some cases at high noise levels there is only a negligible difference between the accuracy of good and bad collective decision rules.

motor-unit

Dependent Accumulation and Underdevelopmentlexity depending on the given bribery/evaluation criteria. Specifically, we show that some of these problems can be solved in polynomial time, some are NP-complete but fixed-parameter tractable, and some are W[2]-complete. Finally, we provide (in)approximability results.

boisterous

Aldo Madariaga,Stefano Palestiniithm UTADIS as a content - based recommendation technique which, in conjunction with collaborative filtering results in two Hybrid Recommendation approaches. The resulting approaches manage to outperform the separate application of the UTADIS and collaborative filtering methods in terms of recommendation accuracy.

加劇

Analyzing Dyadic Data with IRT Modelsies by describing a directed graphical representation that relies on our concept of conditional independence. One advantage of this model is that it leads to a natural structured elicitation process, very similar to that of Bayesian networks.

LIMIT

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