The Chapman & Hall/CRC Machine Learning & Pattern Recognition Series (https://www.crcpress.com/Chapman–HallCRC-Machine-Learning–Pattern-Recognition/book-series/CRCMACLEAPAT)

The field of machine learning has experienced significant growth in the past two decades as new algorithms and techniques have been developed and new research and applications have emerged. This series reflects the latest advances and applications in machine learning and pattern recognition through the publication of a broad range of reference works, textbooks, and handbooks. We are looking for single authored works and edited collections that will:

• Present the latest research and applications in the field, including new mathematical, statistical, and computational methods and techniques
• Provide both introductory and advanced material for students and professionals
• Cover a broad range of topics around learning and inference

The inclusion of concrete examples, applications, and methods is highly encouraged. The scope of the series includes, but is not limited to, titles in the following areas:

• Machine Learning
• Pattern Recognition
• Computational Intelligence
• Robotics
• Computational/Statistical Learning Theory
• Natural Language Processing
• Computer Vision
• Game AI
• Game Theory
• Neural Networks
• Computational Neuroscience.

We are also willing to consider other relevant topics, such as machine learning applied to bioinformatics or cognitive science, which might be proposed by potential contributors.

Series Editor:

Roman V. Yampolskiy

Associate Professor Computer Engineering and Computer Science, University of Louisville

Roman.yampolskiy@louisville.edu

Proposals for the series may be submitted to the series editor or directly to:

Randi Cohen, Publisher CS and IT (randi.cohen@informa.com)

Elliott Morsia, Editor, CS (Elliott.Morsia@tandf.co.uk)

Elliott Morsia, PhD

Commissioning Editor, CRC Press (Computer Science) | https://www.crcpress.com/computer-science-engineering

* Elliott.Morsia@tandf.co.uk | Tel: +44 07468 353 814

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