Statistical Pattern Recognition

Statistical Pattern Recognition
Andrew R. Webb | ISBN: 0-470-84513-9 | 504 pgs | 3 mb

Statistical pattern recognition is a term used to cover all stages of an investigation from problem formulation and data collection through to discrimination and classification, assessment of results and interpretation. Some of the basic terminology is introduced and two complementary approaches to discrimination described.

This book describes basic pattern recognition procedures, together with practical applications of the techniques on real-world problems. A strong emphasis is placed on the statistical theory of discrimination, but clustering also receives some attention. Thus, the subject matter of this book can be summed up in a single word: ‘classification’, both supervised (using class information to design a classifier – i.e. discrimination) and unsupervised (allocating to groups without class information – i.e. clustering).

Pattern recognition as a field of study developed significantly in the 1960s. It was very much an interdisciplinary subject, covering developments in the areas of statistics, engineering, artificial intelligence, computer science, psychology and physiology, among others. Some people entered the field with a real problem to solve. The large numbers of applications, ranging from the classical ones such as automatic character recognition and medical diagnosis to the more recent ones in data mining (such as credit scoring, consumer sales analysis and credit card transaction analysis), have attracted considerable research effort, with many methods developed and advances made. Other researchers were motivated by the development of machines with ‘brain-like’ performance, that in some way could emulate human performance. There were many over-optimistic and unrealistic claims made, and to some extent there exist strong parallels with the growth of research on knowledge-based systems in the 1970s and neural networks in the 1980s.
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