Through the success of artificial neural networks (ANNs) in different domains, intense
research has been recently centered on changing the networks architecture to optimize the …

This survey article describes previous research addressing text‐based document
geocoding, ie the task of predicting the geospatial coordinates of latitude and longitude, that …

Abstract Restricted Boltzmann Machines (RBMs) and models derived from them have been
successfully used as basic building blocks in deep artificial neural networks for automatic …

We propose conducting locally differentially private (LDP) estimation with the aid of a small
amount of public data to enhance the performance of private estimation. Specifically, we …

Decision trees are popular as stand-alone classifiers or as base learners in ensemble
classifiers. Mostly, this is due to decision trees having the advantage of being easy to …

Tree-based models are increasingly popular due to their ability to identify complex
relationships that are beyond the scope of parametric models. Survival tree methods adapt …

This paper concentrates on the different meanings of machine learning (ML) from its origins
to the present and potential future, focusing on contributions within the discipline of …

There are relatively few works dealing with conformal prediction for multi-task learning
issues, and this is particularly true for multi-target regression. This paper focuses on the …

The quality of training data plays a decisive role in the establishment of intelligent models.
Since raw data obtained from the real world are usually entwined with noise due to variety of …

Benchmark experiments are one of the cornerstones of modern machine learning research.
An essential part in the design of such experiments is the selection of datasets. We present …