The interface between the computer and statistical sciences is a rapidly-developing field of research, as each discipline seeks to harness the power and resources of the other. This series aims to capture new developments and summarize what is known over the whole spectrum of computer science and statistics. It seeks to foster the integration of computer science and statistical, numerical and probabilistic methods by publishing a broad range of reference works, textbooks and handbooks.
The scope of the series is wide, including data mining, machine learning, AI, computational stats, exploratory data analysis, pattern recognition, learning theory, statistical software and graphics, graphical models, Bayesian data analysis, and internet data analysis. The titles included in the series are designed to appeal to students, researchers and professionals in computer and information science, statistics, mathematics, and engineering, as well as interdisciplinary researchers across many scientific disciplines. The inclusion of real examples and applications is highly encouraged, as is specific software.
Please contact us if you have an idea for a book for the series.
Exploratory Data Analysis with MATLAB
Pattern Recognition Algorithms for Data Mining
Time Series Clustering and Classification
Textual Data Science with R
Bayesian Regression Modeling with INLA
Chain Event Graphs
Data Science Foundations Geometry and Topology of Complex Hierarchic Systems and Big Data Analytics
By Wendy L. Martinez, Angel R. Martinez, Jeffrey Solka
July 29, 2022
Praise for the Second Edition:"The authors present an intuitive and easy-to-read book. … accompanied by many examples, proposed exercises, good references, and comprehensive appendices that initiate the reader unfamiliar with MATLAB."—Adolfo Alvarez Pinto, International Statistical Review "...
By Steven Abney
September 25, 2019
The rapid advancement in the theoretical understanding of statistical and machine learning methods for semisupervised learning has made it difficult for nonspecialists to keep up to date in the field. Providing a broad, accessible treatment of the theory as well as linguistic applications, ...
By Sankar K. Pal, Pabitra Mitra
September 19, 2019
Pattern Recognition Algorithms for Data Mining addresses different pattern recognition (PR) tasks in a unified framework with both theoretical and experimental results. Tasks covered include data condensation, feature selection, case generation, clustering/classification, and rule generation and ...
By Fionn Murtagh
September 05, 2019
Developed by Jean-Paul Benzérci more than 30 years ago, correspondence analysis as a framework for analyzing data quickly found widespread popularity in Europe. The topicality and importance of correspondence analysis continue, and with the tremendous computing power now available and new fields of...
By Claus Weihs, Olaf Mersmann, Uwe Ligges
June 19, 2019
A new and refreshingly different approach to presenting the foundations of statistical algorithms, Foundations of Statistical Algorithms: With References to R Packages reviews the historical development of basic algorithms to illuminate the evolution of today’s more powerful statistical algorithms....
By Elizabeth Ann Maharaj, Pierpaolo D'Urso, Jorge Caiado
April 12, 2019
The beginning of the age of artificial intelligence and machine learning has created new challenges and opportunities for data analysts, statisticians, mathematicians, econometricians, computer scientists and many others. At the root of these techniques are algorithms and methods for clustering and...
By Brigitte Le Roux, Solène Bienaise, Jean-Luc Durand
February 22, 2019
Geometric Data Analysis designates the approach of Multivariate Statistics that conceptualizes the set of observations as a Euclidean cloud of points. Combinatorial Inference in Geometric Data Analysis gives an overview of multidimensional statistical inference methods applicable to clouds of ...
By Monica Becue-Bertaut
March 01, 2019
Textual Statistics with R comprehensively covers the main multidimensional methods in textual statistics supported by a specially-written package in R. Methods discussed include correspondence analysis, clustering, and multiple factor analysis for contigency tables. Each method is illuminated by ...
By Xiaofeng Wang, Yu Ryan Yue, Julian J. Faraway
February 16, 2018
INLA stands for Integrated Nested Laplace Approximations, which is a new method for fitting a broad class of Bayesian regression models. No samples of the posterior marginal distributions need to be drawn using INLA, so it is a computationally convenient alternative to Markov chain Monte Carlo (...
By Rodrigo A. Collazo, Christiane Goergen, Jim Q. Smith
January 30, 2018
Written by some major contributors to the development of this class of graphical models, Chain Event Graphs introduces a viable and straightforward new tool for statistical inference, model selection and learning techniques. The book extends established technologies used in the study of ...
By Fionn Murtagh
September 07, 2017
"Data Science Foundations is most welcome and, indeed, a piece of literature that the field is very much in need of…quite different from most data analytics texts which largely ignore foundational concepts and simply present a cookbook of methods…a very useful text and I would certainly use it in ...
By Karl Fraser, Zidong Wang, Xiaohui Liu
June 14, 2017
To harness the high-throughput potential of DNA microarray technology, it is crucial that the analysis stages of the process are decoupled from the requirements of operator assistance. Microarray Image Analysis: An Algorithmic Approach presents an automatic system for microarray image processing to...