Chapman & Hall/CRC Machine Learning & Pattern Recognition
About the Book 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]).
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...
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
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 ...
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 ...