184 pp., Paperback, previous owner's name to verso of the front cover else very good. - If you are reading this, this item is actually (physically) in our stock and ready for shipment once ordered. We are not bookjackers. Buyer is responsible for any additional duties, taxes, or fees required by recipient's country.

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Bibliographic Details

Title: Matrix Methods in Data Mining and Pattern Recognition (Fundamentals of Algorithms, Series Number 4)
Publisher: Society for Industrial and Applied Mathematics
Publication Date: 2007
Language: English
ISBN 10: 0898716268
ISBN 13: 9780898716269
Binding: Soft cover
Condition: Very Good

About this title

Synopsis

Several very powerful numerical linear algebra techniques are available for solving problems in data mining and pattern recognition. This application-oriented book describes how modern matrix methods can be used to solve these problems, gives an introduction to matrix theory and decompositions, and provides students with a set of tools that can be modified for a particular application. Part I gives a short introduction to a few application areas before presenting linear algebra concepts and matrix decompositions that students can use in problem-solving environments such as MATLAB. In Part II, linear algebra techniques are applied to data mining problems. Part III is a brief introduction to eigenvalue and singular value algorithms. The applications discussed include classification of handwritten digits, text mining, text summarization, pagerank computations related to the Google search engine, and face recognition. Exercises and computer assignments are available on a Web page that supplements the book.

About the Author

Lars Eld�n is professor of numerical analysis at Link�ping University in Sweden.

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