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Chapman & Hall/CRC Machine Learning & Pattern Recognition


About the Series

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 areas of machine learning, pattern recognition, computational intelligence, robotics, computational/statistical learning theory, natural language processing, computer vision, game AI, game theory, neural networks, and 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.

For more information or to submit a book proposal for the series, please contact Randi Cohen, Publisher, CS and IT ([email protected]) or Elliott Morsia, Editor, CS ([email protected]).

20 Series Titles

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Computational Trust Models and Machine Learning

Computational Trust Models and Machine Learning

1st Edition

Edited By Xin Liu, Anwitaman Datta, Ee-Peng Lim
October 29, 2014

Computational Trust Models and Machine Learning provides a detailed introduction to the concept of trust and its application in various computer science areas, including multi-agent systems, online social networks, and communication systems. Identifying trust modeling challenges that cannot be ...

Regularization, Optimization, Kernels, and Support Vector Machines

Regularization, Optimization, Kernels, and Support Vector Machines

1st Edition

Edited By Johan A.K. Suykens, Marco Signoretto, Andreas Argyriou
October 23, 2014

Regularization, Optimization, Kernels, and Support Vector Machines offers a snapshot of the current state of the art of large-scale machine learning, providing a single multidisciplinary source for the latest research and advances in regularization, sparsity, compressed sensing, convex and ...

Machine Learning An Algorithmic Perspective, Second Edition

Machine Learning: An Algorithmic Perspective, Second Edition

2nd Edition

By Stephen Marsland
October 08, 2014

A Proven, Hands-On Approach for Students without a Strong Statistical Foundation Since the best-selling first edition was published, there have been several prominent developments in the field of machine learning, including the increasing work on the statistical interpretations of machine learning...

Bayesian Programming

Bayesian Programming

1st Edition

By Pierre Bessiere, Emmanuel Mazer, Juan Manuel Ahuactzin, Kamel Mekhnacha
December 20, 2013

Probability as an Alternative to Boolean LogicWhile logic is the mathematical foundation of rational reasoning and the fundamental principle of computing, it is restricted to problems where information is both complete and certain. However, many real-world problems, from financial investments to ...

Multilinear Subspace Learning Dimensionality Reduction of Multidimensional Data

Multilinear Subspace Learning: Dimensionality Reduction of Multidimensional Data

1st Edition

By Haiping Lu, Konstantinos N. Plataniotis, Anastasios Venetsanopoulos
December 11, 2013

Due to advances in sensor, storage, and networking technologies, data is being generated on a daily basis at an ever-increasing pace in a wide range of applications, including cloud computing, mobile Internet, and medical imaging. This large multidimensional data requires more efficient ...

Multi-Label Dimensionality Reduction

Multi-Label Dimensionality Reduction

1st Edition

By Liang Sun, Shuiwang Ji, Jieping Ye
November 04, 2013

Similar to other data mining and machine learning tasks, multi-label learning suffers from dimensionality. An effective way to mitigate this problem is through dimensionality reduction, which extracts a small number of features by removing irrelevant, redundant, and noisy information. The data ...

Ensemble Methods Foundations and Algorithms

Ensemble Methods: Foundations and Algorithms

1st Edition

By Zhi-Hua Zhou
June 06, 2012

An up-to-date, self-contained introduction to a state-of-the-art machine learning approach, Ensemble Methods: Foundations and Algorithms shows how these accurate methods are used in real-world tasks. It gives you the necessary groundwork to carry out further research in this evolving field. After ...

Handbook of Natural Language Processing

Handbook of Natural Language Processing

2nd Edition

Edited By Nitin Indurkhya, Fred J. Damerau
February 22, 2010

The Handbook of Natural Language Processing, Second Edition presents practical tools and techniques for implementing natural language processing in computer systems. Along with removing outdated material, this edition updates every chapter and expands the content to include emerging areas, such as ...

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