Titlebook: Belief Functions: Theory and Applications; 7th International Co Sylvie Le Hégarat-Mascle,Isabelle Bloch,Emanuel Al Conference proceedings 2

的是兄弟

Industrialismus und Ökoromantiknce. We show that in the particular case where the focal sets of the belief function are Cartesian products of intervals, finding best, ., non-dominated, paths according to these criteria amounts to solving known variants of the deterministic shortest path problem, for which exact resolution algorithms exist.

braggadocio

Evidential Clustering by Competitive Agglomeration since it can mine the ambiguity and uncertainty of the data structure; secondly, through a competitive strategy, it can automatically gain the number of clusters under the rule of intra-class compactness and inter-class dispersion. Results demonstrate the effectiveness of the proposed method on synthetic and real-world datasets.

cutlery

Belief Functions on Ordered Frames of Discernmentisjunctive combination. We also study distances on ordered elements and their use. In particular, from a membership function, we redefine the cardinality of the intersection of ordered elements, considering them fuzzy.

极端的正确性

从属

A Variational Bayesian Clustering Approach to Acoustic Emission Interpretation Including Soft Labelsused in non-destructive testing. This model, called VBGMM (variational Bayesian GMM) allows the end-user to automatically determine the number of clusters which makes it relevant for this type of application where clusters are related to damages. In this work, we modify the training procedure to inc

FACT

paleolithic

慷慨不好

发炎

Ordinal Classification Using Single-Model Evidential Extreme Learning Machine theory, in this paper, the single-model multi-output extreme learning machine is learned from evidential training data. Taking both the uncertainty and the ordering relation of labels into consideration, given mass functions of training labels, different evidential encoding schemes for model output

Madrigal

Reliability-Based Imbalanced Data Classification with Dempster-Shafer Theorythe minority class. This paper proposes a reliability-based imbalanced data classification approach (RIC) with Dempster-Shafer theory to address this issue. First, based on the minority class, multiple under-sampling for the majority one are implemented to obtain the corresponding balanced training