Pages that link to "Item:Q274435"
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The following pages link to Prediction of future observations using belief functions: a likelihood-based approach (Q274435):
Displaying 19 items.
- Canonical decomposition of belief functions based on Teugels' representation of the multivariate Bernoulli distribution (Q781291) (← links)
- Face pixel detection using evidential calibration and fusion (Q1679665) (← links)
- SELP: semi-supervised evidential label propagation algorithm for graph data clustering (Q1687278) (← links)
- Frequency-calibrated belief functions: review and new insights (Q1687287) (← links)
- Cautious classification based on belief functions theory and imprecise relabelling (Q2076976) (← links)
- Modeling random and non-random decision uncertainty in ratings data: a fuzzy beta model (Q2125738) (← links)
- Forecasting using information and entropy based on belief functions (Q2205960) (← links)
- Evidential joint calibration of binary SVM classifiers (Q2318267) (← links)
- Constructing consonant belief functions from sample data using confidence sets of pignistic probabilities (Q2379336) (← links)
- Utilizing belief functions for the estimation of future climate change (Q2386116) (← links)
- Parametric classification with soft labels using the evidential EM algorithm: linear discriminant analysis versus logistic regression (Q2418335) (← links)
- Forecasting using belief functions: an application to marketing econometrics (Q2447771) (← links)
- Monte Carlo and quasi-Monte Carlo methods for Dempster's rule of combination (Q2671754) (← links)
- Significance test for linear regression: how to test without <i>P</i>-values? (Q5861567) (← links)
- Belief functions induced by random fuzzy sets: a general framework for representing uncertain and fuzzy evidence (Q6081358) (← links)
- Reasoning with fuzzy and uncertain evidence using epistemic random fuzzy sets: general framework and practical models (Q6083111) (← links)
- Active Evidential Calibration of Binary SVM Classifiers (Q6108665) (← links)
- Uncertainty quantification in logistic regression using random fuzzy sets and belief functions (Q6548469) (← links)
- Synergies between machine learning and reasoning -- an introduction by the Kay R. Amel group (Q6577680) (← links)
