Statistical Pattern Recognition

Statistical pattern recognition relates to the use of statisticaltechniques for analysing data measurements in order to extractinformation and make justified decisions. It is a very activearea of study and research, which has seen many advances in recentyears. Applications such as data mining, web searching, multimediadata retrieval, face recognition, and cursive handwritingrecognition, all require robust and efficient pattern recognitiontechniques.

This third edition provides an introduction to statisticalpattern theory and techniques, with material drawn from a widerange of fields, including the areas of engineering, statistics,computer science and the social sciences. The book has been updatedto cover new methods and applications, and includes a wide range oftechniques such as Bayesian methods, neural networks, supportvector machines, feature selection and feature reductiontechniques.
Technical descriptions and motivations are provided, andthe techniques are illustrated using real examples.

Statistical Pattern Recognition, 3rdEdition:
* Provides a self-contained introduction to statistical patternrecognition.
* Includes new material presenting the analysis of complexnetworks.
* Introduces readers to methods for Bayesian densityestimation.
* Presents descriptions of new applications in biometrics,security, finance and condition monitoring.
* Provides descriptions and guidance for implementing techniques,which will be invaluable to software engineers and developersseeking to develop real applications
* Describes mathematically the range of statistical patternrecognition techniques.
* Presents a variety of exercises including more extensivecomputer projects.

The in-depth technical descriptions make the book suitable forsenior undergraduate and graduate students in statistics, computerscience and engineering. Statistical PatternRecognition is also an excellent reference source for technicalprofessionals. Chapters have been arranged to facilitateimplementation of the techniques by software engineers anddevelopers in non-statistical engineering fields.

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