Regularization Techniques for Machine Learning and Their Applications

Dear Colleagues,

We invite you to submit your latest research in the development of ensemble algorithms to this Special Issue, “Regularization Techniques for Machine Learning and Their Applications”.

Over the last decade, learning theory has led to the achievement of significant progress in the development of sophisticated algorithms and their theoretical foundations. The theory builds on concepts which exploit ideas and methodologies from mathematical areas, such as optimization theory. Regularization is probably the key to address the challenging problem of overfitting, which usually occurs in high-dimensional learning. Its primary goal is to make the machine learning algorithm “learn” and not “memorize” by penalizing the algorithm to reduce its generalization error in order to avoid the risk of overfitting. As a result, the variance of the model is significantly reduced, without substantial increase in its bias and without losing any important properties in the data. 

The main aim of this Special Issue is to present the recent advances related to all kinds of regularization methodologies and investigations of the impact of their application to a diversity of real-world problems.

Prof. Dr. Theodore Kotsilieris
Dr. Ioannis E. Livieris
Prof. Dr. Ioannis Anagnostopoulos
Guest Editors

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