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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.

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Data Science and Machine Learning: Mathematical and Statistical Methods

Data Science and Machine Learning: Mathematical and Statistical Methods

1st Edition

Forthcoming

Dirk P. Kroese, Zdravko Botev, Thomas Taimre, Radislav Vaisman
November 21, 2019

"This textbook is a well-rounded, rigorous, and informative work presenting the mathematics behind modern machine learning techniques. It hits all the right notes: the choice of topics is up-to-date and perfect for a course on data science for mathematics students at the advanced undergraduate or...

A Concise Introduction to Machine Learning

A Concise Introduction to Machine Learning

1st Edition

A.C. Faul
August 12, 2019

The emphasis of the book is on the question of Why – only if why an algorithm is successful is understood, can it be properly applied, and the results trusted. Algorithms are often taught side by side without showing the similarities and differences between them. This book addresses the...

Introduction to Machine Learning with Applications in Information Security

Introduction to Machine Learning with Applications in Information Security

1st Edition

Mark Stamp
September 07, 2017

Introduction to Machine Learning with Applications in Information Security provides a class-tested introduction to a wide variety of machine learning algorithms, reinforced through realistic applications. The book is accessible and doesn’t prove theorems, or otherwise dwell on mathematical theory....

A First Course in Machine Learning

A First Course in Machine Learning

2nd Edition

Simon Rogers, Mark Girolami
August 15, 2016

"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

Statistical Reinforcement Learning: Modern Machine Learning Approaches

1st Edition

Masashi Sugiyama
March 16, 2015

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...

Sparse Modeling: Theory, Algorithms, and Applications

Sparse Modeling: Theory, Algorithms, and Applications

1st Edition

Irina Rish, Genady Grabarnik
December 01, 2014

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,...

Computational Trust Models and Machine Learning

Computational Trust Models and Machine Learning

1st Edition

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

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

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

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

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

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...

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