Tchamova A., On the validity of Dempster's fusion rule and its interpretation as a generalization of Bayesian fusion rule, Int. J. of Intell. Sys., Vol. 29, pp. 223-252, 2014.J. Dezert, A. Tchamova, On the validity of Dempster's fusion rule and its interpretation as a ...
On the Complexity of Bayesian Generalization 贝叶斯泛化的复杂性 https://proceedings.mlr.press/v202/shi23i/shi23i.pdf 摘要 我们研究了自然视觉光谱中大规模概念泛化的情况。现有的计算模式(即基于规则或基于相似性的模式)主要在隔离状态下被研究,聚焦于有限且抽象的问题空间。在本研究中,我们探讨了当问题空间...
1988-NIPS-A back-propagation algorithm with optimal use of hidden units 1988-NIPS-Skeletonization: A Technique for Trimming the Fat from a Network via Relevance Assessment 1988-NIPS-What Size Net Gives Valid Generalization? 1989-NIPS-Dynamic Behavior of Constained Back-Propagation Networks 1988-NIPS...
(2021 NeurIPS) DiBS: Differentiable Bayesian Structure Learning. Lars Lorch, Jonas Rothfuss, Bernhard Schölkopf, Andreas Krause. [pdf] (2021 ICML) Necessary and sufficient conditions for causal feature selection in time series with latent common causes. Atalanti A Mastakouri, Bernhard Schölkopf,...
The present study examines the role of feature selection methods in optimizing machine learning algorithms for predicting heart disease. The Cleveland Heart disease dataset with sixteen feature selection techniques in three categories of filter, wrapper,
Due to the rapid proleferation of these AI models, explaining their learning and decision making process are getting harder which require transparency and easy predictability. Aiming to collate the current state-of-the-art in interpreting the black-box models, this study provides a comprehensive ...
the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC), as noted by George et al. (2014). We thus estimated all four models, and all of the criteria indicated that the Log-normal model is a good fit for our data.Footnote19Our choice is also supported by the ...
impulse noise was introduced. The key innovation lies in the adaptive weighting of a data-fidelity term within the cost function. This term, derived using statistical methods, comprises two weighting functions and statistical control parameters for noise. Then a Bayesian framework is formulated where ...
(Rubin2008). In Bayesian statistics, other information can be incorporated asa priori information(e.g., O’Hagan2004). This might stem from previous studies or might be based on expert opinions. The prior information combined with the data (likelihood) then results in a posterior distribution. ...
The present method aims to determine the parameters of the model such that pyi|xi,xi′ is being maximized for each training Theoretical analysis This section first discusses the time complexity of the proposed algorithm and then presents some theoretical results on the generalization of the proposed...