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]).
Computational Trust Models and Machine Learning
1st Edition
Edited
By Xin Liu, Anwitaman Datta, Ee-Peng Lim
December 18, 2020
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
1st Edition
Edited
By Johan A.K. Suykens, Marco Signoretto, Andreas Argyriou
September 30, 2020
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 ...
Sparse Modeling: Theory, Algorithms, and Applications
1st Edition
By Irina Rish, Genady Grabarnik
September 30, 2020
Sparse models are particularly useful in scientific applications, such as biomarker discovery in genetic or neuroimaging data, where the interpretability of a predictive model is essential. Sparsity can also dramatically improve the cost efficiency of signal processing.Sparse Modeling: Theory, ...
A First Course in Machine Learning
2nd Edition
By Simon Rogers, Mark Girolami
June 30, 2020
"A First Course in Machine Learning by Simon Rogers and Mark Girolami is the best introductory book for ML currently available. It combines rigor and precision with accessibility, starts from a detailed explanation of the basic foundations of Bayesian analysis in the simplest of settings, and goes ...
Statistical Reinforcement Learning: Modern Machine Learning Approaches
1st Edition
By Masashi Sugiyama
June 30, 2020
Reinforcement learning is a mathematical framework for developing computer agents that can learn an optimal behavior by relating generic reward signals with its past actions. With numerous successful applications in business intelligence, plant control, and gaming, the RL framework is ideal for ...
Utility-Based Learning from Data
1st Edition
By Craig Friedman, Sven Sandow
November 25, 2019
Utility-Based Learning from Data provides a pedagogical, self-contained discussion of probability estimation methods via a coherent approach from the viewpoint of a decision maker who acts in an uncertain environment. This approach is motivated by the idea that probabilistic models are usually not ...
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 ...