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Although non-parametric tests have already been proposed for that purpose, statistical significance tests for non-standard measures (different from the classification error) are less often used in the literature. This paper is an attempt at empirically ver ...
Although non-parametric tests have already been proposed for that purpose, statistical significance tests for non-standard measures (different from the classification error) are less often used in the literature. This paper is an attempt at empirically ver ...
We outline how modern likelihood theory, which provides essentially exact inferences in a variety of parametric statistical problems, may routinely be applied in practice. Although the likelihood procedures are based on analytical asymptotic approximations ...
In this paper we aim to explore what is the most appropriate number of data samples needed when measuring the temporal correspondence between a chosen set of video and audio cues in a given audio-visual sequence. Presently the optimal model that connects s ...
The performance of many color science and imaging algorithms are evaluated based on their mean errors. However, if these errors are not normally distributed, statistical evaluations based on the mean are not appropriate performance metrics. We present a no ...
The main scope of this project is to identify the best method of confidence estimator whose performance could be reliable in comparison to multimodal fusion alone. To do that, three alternative approaches to prediction confidence estimation are presented a ...
We propose a deep study on tissue modelization and classification techniques on T1-weighted MR images. Six approaches have been taken into account to perform this validation study. We consider first the Finite Gaussian Mixture model (A-FGMM) and a Bayes cl ...
A non-parametric method of distribution estimation for univariate data is presented. The idea is to adapt the smoothing spline procedure used in regression to the estimation of distributions via a scatterplot smoothing of theempirical distribution function ...
Non-parametric models and techniques enjoy a growing popularity in the field of machine learning, and among these Bayesian inference for Gaussian process (GP) models has recently received significant attention. We feel that GP priors should be part of the ...
