Table of Contents Author Guidelines Submit a Manuscript

Learning Theory

Call for Papers

Machine learning is extensively used in commercial systems and industrial companies ranging from computer vision and bioinformatics to social web mining. As the field has evolved, there has been an increased emphasis on understanding its mathematical underpinnings. There is no doubt that machine learning has rapidly progressed from a discipline studied primarily by researchers in artificial intelligence to a much broader discipline also studied by applied mathematicians and statisticians.

The main focus of this special issue will be on theoretical aspects of machine learning algorithms. The special issue will serve as an international forum for researches into machine learning and applied mathematics and statistics to summarize the most recent developments and ideas in the fast developing field, with a special emphasis given to learning theory. Both original research articles and review articles are welcome. Potential topics include, but are not limited to:

  • Generalization and consistency of learning algorithms
  • Approximation theory related to learning theory
  • Characterization of reproducing kernel Hilbert space (RKHS)
  • Concentration inequality and empirical process theory for analyzing learning algorithms
  • Other theoretical analysis of novel machine learning methods

Before submission authors should carefully read over the journal's Author Guidelines, which are located at Prospective authors should submit an electronic copy of their complete manuscript through the journal Manuscript Tracking System at according to the following timetable:

Manuscript DueFriday, 11 October 2013
First Round of ReviewsFriday, 3 January 2014
Publication DateFriday, 28 February 2014

Lead Guest Editor

  • Ding-Xuan Zhou, Department of Mathematics, City University of Hong Kong, Hong Kong

Guest Editors

  • Qiang Wu, Department of Mathematical Sciences, Middle Tennessee State University, Murfreesboro, TN, USA
  • Yiming Ying, College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK