| Statistical and Neural Classifiers: An Integrated Approach to Design 2001 Edition Contributor(s): Raudys, Sarunas (Author) |
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ISBN: 1852332972 ISBN-13: 9781852332976 Publisher: Springer OUR PRICE: $161.49 Product Type: Hardcover - Other Formats Published: January 2001 Annotation: The classification of patterns is an important area of research which is central to all pattern recognition fields, including speech, image, robotics, and data analysis. Neural networks have been used successfully in a number of these fields, but so far their application has been based on a "black box approach," with no real understanding of how they work. In this book, Sarunas Raudys - an internationally respected researcher in the area - provides an excellent mathematical and applied introduction to how neural network classifiers work and how they should be used to optimal effect. Among the topics covered are: - Different types of neural network classifiers; - A taxonomy of pattern classification algorithms; - Performance capabilities and measurement procedures; - Which features should be extracted from raw data for the best classification results. This book will provide essential reading for anyone researching or studying relevant areas of pattern recognition (such as image processing, speech recognition, robotics, and multimedia). It will also be of interest to anyone studing or researching in applied neural networks. |
| Additional Information |
| BISAC Categories: - Computers | Neural Networks - Computers | Intelligence (ai) & Semantics |
| Dewey: 006.4 |
| LCCN: 00045035 |
| Series: Advances in Pattern Recognition |
| Physical Information: 0.75" H x 6.14" W x 9.21" (1.38 lbs) 295 pages |
| Descriptions, Reviews, Etc. |
| Publisher Description: Automatic (machine) recognition, description, classification, and groupings of patterns are important problems in a variety of engineering and scientific disciplines such as biology, psychology, medicine, marketing, computer vision, artificial intelligence, and remote sensing. Given a pattern, its recognition/classification may consist of one of the following two tasks: (1) supervised classification (also called discriminant analysis); the input pattern is assigned to one of several predefined classes, (2) unsupervised classification (also called clustering); no pattern classes are defined a priori and patterns are grouped into clusters based on their similarity. Interest in the area of pattern recognition has been renewed recently due to emerging applications which are not only challenging but also computationally more demanding (e. g., bioinformatics, data mining, document classification, and multimedia database retrieval). Among the various frameworks in which pattern recognition has been traditionally formulated, the statistical approach has been most intensively studied and used in practice. More recently, neural network techniques and methods imported from statistical learning theory have received increased attention. Neural networks and statistical pattern recognition are two closely related disciplines which share several common research issues. Neural networks have not only provided a variety of novel or supplementary approaches for pattern recognition tasks, but have also offered architectures on which many well-known statistical pattern recognition algorithms can be mapped for efficient (hardware) implementation. On the other hand, neural networks can derive benefit from some well-known results in statistical pattern recognition. |