Bibliothèque de L'IMIST/CNRST

Includes bibliographical references and index.

Introduction to statistical pattern recognition -- Density estimation-parametric -- Density estimation-Bayesian -- Density estimation-nonparametric -- Linear discriminant analysis -- Nonlinear discriminant analysis-kernel and projection methods -- Rule and decision tree induction -- Ensemble methods -- Performance assessment -- Feature selection and extraction -- Clustering -- Complex networks -- Additional topics.

"Statistical Pattern Recognition provides an introduction to statistical pattern theory and techniques, with material drawn from a wide range of fields, including the areas of engineering, statistics, computer science and the social sciences. The book describes techniques for analysing data comprising measurements made on individuals or objects. The techniques are used to make a prediction such as disease of a patient, the type of object illuminated by a radar, economic forecast. Emphasis is placed on techniques for classification, a term used for predicting the class or group an object belongs to (based on a set of exemplars) and for methods that seek to discover natural groupings in a data set. Each section concludes with a description of the wide range of practical applications that have been addressed and the further developments of theoretical techniques and includes a variety of exercises, from 'open-book' questions to more lengthy projects. New material is presented, including the analysis of complex networks and basic techniques for analysing the properties of datasets and also introduces readers to the use of variational methods for Bayesian density estimation and looks at new applications in biometrics and security."--

"The book describes techniques for analysing data comprising measurements made on individuals or objects."--