Titlebook: Belief Functions: Theory and Applications; 8th International Co Yaxin Bi,Anne-Laure Jousselme,Thierry Denoeux Conference proceedings 2024 T

尖牙

Steel symbol/number: DC04/1.0338,del is fit by minimizing a generalized negative log-likelihood function that accounts for both normal and censored data. Comparative experiments on two real-world datasets demonstrate the very good performance of our model as compared to the state-of-the-art.

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Steel symbol/number: DC04/1.0338,ecision theory, our work builds on these connections. In our paper, we establish pointwise and uniform consistency of an . as an approximation to the true risk function via the derivation of nonasymptotic concentration bounds, and our work serves as the foundation for future investigations of the properties of the MFGF upper risk.

Apraxia

使人煩燥

軌道

GLOOM

Incremental Belief-Peaks Evidential Clusteringation in the realm of big data remains constrained by excessive computational complexity and limited computational resources. To bridge this research gap, this paper introduces an .ncremental .vidential .lustering (IEC) method based on stream data clustering and belief-peaks, a technique that has de

Prophylaxis

AMEND

Dempster-Shafer Credal Probabilistic Circuitsications do not fully account for epistemic uncertainty. To address this, credal probabilistic circuits were introduced, incorporating a way to manage such uncertainty. We propose a novel framework for learning the structure and parameters of credal probabilistic circuits, leveraging the Dempster-Sh

非實(shí)體

Uncertainty Quantification in?Regression Neural Networks Using Likelihood-Based Belief Functions is based on the Gaussian approximation of the likelihood function and the linearization of the network output with respect to the weights. Prediction uncertainty is described by a random fuzzy set inducing a predictive belief function. Preliminary experiments suggest that the approximations are ver