Minimum error entropy (MEE) is an information theoretic learning approach that minimizes
the information contained in the prediction error, which is measured by entropy. It has been …

We introduce a learning algorithm for regression generated by a minimum error entropy
(MEE) principle and regularization schemes in reproducing kernel Hilbert spaces. This …

Distributed learning based on the divide and conquer approach is a powerful tool for big
data processing. We introduce a distributed kernel gradient descent algorithm for the …

In this paper we study the consistency of an empirical minimum error entropy (MEE)
algorithm in a regression setting. We introduce two types of consistency. The error entropy …

The learning process of a multilayer perceptron requires the optimization of an error function
E (y, t) comparing the predicted output, y, and the observed target, t. We review some usual …

Data Mining for Business Applications presents the state-of-the-art research and
development outcomes on methodologies, techniques, approaches and successful …

Classification of streaming data faces three basic challenges: it has to deal with huge
amounts of data, the varying time between two stream data items must be used best …

We study the convergence of minimum error entropy (MEE) algorithms when they are
implemented by gradient descent. This method has been used in practical applications for …

The enormous number of articles published daily on the Internet, by a diverse array of
authors, often offers misleading or unwanted information, rendering activities such as sports …

Federated Learning (FL) has emerged as a prominent distributed machine learning
framework that enables geographically discrete clients to train a global model …