Электронная книга: Webb Andrew R. «Statistical Pattern Recognition»
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Statistical pattern recognition relates to the use of statistical techniques for analysing data measurements in order to extract information and make justified decisions. It is a very active area of study and research, which has seen many advances in recent years. Applications such as data mining, web searching, multimedia data retrieval, face recognition, and cursive handwriting recognition, all require robust and efficient pattern recognition techniques. This third edition 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 has been updated to cover new methods and applications, and includes a wide range of techniques such as Bayesian methods, neural networks, support vector machines, feature selection and feature reduction techniques.Technical descriptions and motivations are provided, and the techniques are illustrated using real examples. Statistical Pattern Recognition, 3rd Edition: Provides a self-contained introduction to statistical pattern recognition. Includes new material presenting the analysis of complex networks. Introduces readers to methods for Bayesian density estimation. 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 developers seeking to develop real applications Describes mathematically the range of statistical pattern recognition techniques. Presents a variety of exercises including more extensive computer projects. The in-depth technical descriptions make the book suitable for senior undergraduate and graduate students in statistics, computer science and engineering. Statistical Pattern Recognition is also an excellent reference source for technical professionals. Chapters have been arranged to facilitate implementation of the techniques by software engineers and developers in non-statistical engineering fields. www.wiley.com/go/statistical_pattern_recognition Издательство: "John Wiley&Sons Limited"
ISBN: 9781119952961 электронная книга Купить за 11785.16 руб и скачать на Litres |
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См. также в других словарях:
Pattern recognition — is a sub topic of machine learning. It is the act of taking in raw data and taking an action based on the category of the data .citation needed|date=September 2008 Most research in pattern recognition is about methods for supervised learning and… … Wikipedia
Syntactic pattern recognition — or structural pattern recognition is a form of pattern recognition, where items are presented pattern structures which can take into account more complex interrelationships between features than simple numerical feature vectors used in… … Wikipedia
Features (pattern recognition) — In pattern recognition, features are the individual measurable heuristic properties of the phenomena being observed. Choosing discriminating and independent features is key to any pattern recognition algorithm being successful in classification.… … Wikipedia
Prior knowledge for pattern recognition — Pattern recognition is a very active field of research intimately bound to machine learning. Also known as classification or statistical classification, pattern recognition aims at building a classifier that can determine the class of an input… … Wikipedia
Multispectral pattern recognition — Multispectral remote sensing is the collection and analysis of reflected, emitted, or back scattered energy from an object or an area of interest in multiple bands of regions of the electromagnetic spectrum (Jensen, 2005). Subcategories of… … Wikipedia
Statistical classification — See also: Pattern recognition See also: Classification test In machine learning, statistical classification is the problem of identifying the sub population to which new observations belong, where the identity of the sub population is unknown, on … Wikipedia
