The goal of ensemble regression is to combine several models in order to improve the
prediction accuracy in learning problems with a numerical target variable. The process of …

We introduce AutoGluon-Tabular, an open-source AutoML framework that requires only a
single line of Python to train highly accurate machine learning models on an unprocessed …

Diversity among the members of a team of classifiers is deemed to be a key issue in
classifier combination. However, measuring diversity is not straightforward because there is …

Ensembles of learning machines constitute one of the main current directions in machine
learning research, and have been applied to a wide range of real problems. Despite of the …

Broad classes of statistical classification algorithms have been developed and applied
successfully to a wide range of real-world domains. In general, ensuring that the particular …

Structural defects control the kinetic, thermodynamic and mechanical properties of glasses.
For instance, rare quantum tunneling two-level systems (TLS) govern the physics of glasses …

Neural computing has moved beyond simple demonstration to more significant applications.
Encouraged by recent developments in artificial neural network (ANN) modelling …

Several researchers have experimentally shown that substantial improvements can be
obtained in difficult pattern recognition problems by combining or integrating the outputs of …

This article explores the explicative capabilities of neural network classifiers with automatic
relevance determination weight regularization, and reports the findings from applying these …

The model selection strategy is an important determinant of the performance and
acceptance of a medical diagnostic decision support system based on supervised learning …