Abstract: This paper reconstructs multivariate functions from scattered data by a new multiscale technique. The reconstruction uses support vector regression model by positive definite reproducing ...

Abstract: We consider the problem of learning smooth multivariate probability density functions. We invoke the canonical decomposition of multivariate functions and we show that if a joint probability ...

Functional connectivity analyses are typically based on matrices containing bivariate measures of covariability, such as correlations. Although this has been a fruitful approach, it may not be the ...

Kolmogorov-Arnold Networks (KANs) have very recently been proposed as a great breakthrough in Deep Learning technologies, enabling both better accuracy and enhanced interpretability than traditional ...

Recent advances in estimation techniques have underscored the growing importance of shrinkage estimation and balanced loss functions in the analysis of multivariate normal distributions. These ...

In this repository, we have uploaded the code corresponding to the CIKM2020 Paper "Analysis of Multivariate Scoring Functions for Automatic Unbiased Learning to Rank ...

Multivariate analysis is a statistical technique that deals with multiple variables simultaneously to explore their interdependence. It is a broad term that can help answer questions such as how ...

WindowData() is a new function that allows users to segment data (univariate or multivariate time series) into windows with/without overlapping samples! This allows users to calculate entropy on ...