| ECVision indexed and annotated bibliography of cognitive computer vision publications This bibliography was created by Hilary Buxton and Benoit Gaillard, University of Sussex, as part of ECVision Specific Action 8-1 The complete text version of this BibTeX file is available here: ECVision_bibliography.bib
B. Ripley
ABSTRACT
Pattern recognition has long been studied in relation to many different (and mainly unrelated) applications, such as classifying galaxies by shape, identifying fingerprints, highlighting potential tumours on a mammogram, handwriting recognition. Human expertise in these and many similar problems is being supplemented by computer-based procedures, especially neural networks. Pattern recognition is extremely widely used, often under the names of `classification', `diagnosis' or `learning from examples'. The methods are often very successful, and this book explains why. It is an in-depth study of methods for pattern recognition drawn from engineering, statistics, machine learning and neural networks. All the modern branches of the subject are covered, together with case studies of applications. The relevant parts of statistical decision theory and computational learning theory are included, as well as methods such as feed-forward neural networks (multi-layer perceptrons), radial basis functions, learning vector quantization and Kohonen's self-organizing maps. The theory explains how to tune and assess the methods. Methods are illustrated by case studies on real examples, the data for which are available over the Internet. Comprehensive account of the theory, including new, simplified, proofs. New insights from integrating ideas from different disciplines. Includes belief nets and probabilistic expert systems. This is a self-contained account, ideal both as an introduction for non-specialists and also as a handbook for the more expert reader. |
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