Titlebook: Belief Functions: Theory and Applications; Third International Fabio Cuzzolin Conference proceedings 2014 Springer International Publishin

阻礙

CLEAR

978-3-319-11190-2Springer International Publishing Switzerland 2014

Mercurial

Duodenitis

Outshine

The Choice of Generalized Dempster-Shafer Rules for Aggregating Belief Functions Based on Imprecisiotions. The approach is based on measuring various types of uncertainty in information and we use for this linear imprecision indices. Some results concerning properties of such rules are also presented.

泥土謙卑

General Schemes of Combining Rules and the Quality Characteristics of Combiningicient conditions of change of ignorance when evidences are combined with the help of various rules. It is shown that combining rules can be regarded as pessimistic or optimistic depending on the sign of the change of ignorance after applying.

外向者

An Optimal Unified Combination Rulevidence. It is optimal in the sense that the resulting combined .-function has the least dissimilarity with the individual .-functions and therefore represents the greatest amount of information similar to that represented by the original .-functions. Examples are provided to illustrate the proposed

excrete

Evidential Logistic Regression for Binary SVM Classifier Calibrationd when dealing with high uncertainty. Many classification approaches such as .-nearest neighbors, neural network or decision trees have been formulated with belief functions. In this paper, we propose an evidential calibration method that transforms the output of a classifier into a belief function.

胖人手藝好

Valves

Belief Hierarchical Clusteringals with a new approach to cluster uncertain data by using a hierarchical clustering defined within the belief function framework. The main objective of the belief hierarchical clustering is to allow an object to belong to one or several clusters. To each belonging, a degree of belief is associated,