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Matrix methods in data mining and pa...
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Elden, Lars, (1944-)
Matrix methods in data mining and pattern recognition /
Record Type:
Language materials, printed : Monograph/item
Title/Author:
Matrix methods in data mining and pattern recognition // Lars Elden.
Author:
Elden, Lars,
Published:
Philadelphia, PA :Society for Industrial and Applied Mathematics, : c2007.,
Description:
x, 224 p. :ill. ; : 26 cm.;
Subject:
Data mining. -
ISBN:
9780898716269 (pbk.) :
Matrix methods in data mining and pattern recognition /
Elden, Lars,1944-
Matrix methods in data mining and pattern recognition /
Lars Elden. - Philadelphia, PA :Society for Industrial and Applied Mathematics,c2007. - x, 224 p. :ill. ;26 cm. - Fundamentals of algorithms. - Fundamentals of algorithms..
Includes bibliographical references (p. 209-216) and index.
I: Linear algebra concepts and matrix decompositions -- 1: Vectors and matrices in data mining and pattern recognition -- 2: Vectors and matrices -- 3: Linear systems and least squares -- 4: Orthogonality -- 5: QR decomposition -- 6: Singular value decomposition -- 7: Reduced-rank least squares models -- 8: Tensor decomposition -- II: Data mining applications -- 10: Classification of handwritten digits -- 11: Text mining -- 12: Page ranking for a Web search engine -- 13: Automatic key word and key sentence extraction -- 14: Face recognition using tensor SVD -- III: Computing the matrix decompositions -- 15: Computing eigenvalues and singular values.
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. The book is intended for undergraduate students who have previously taken an introductory scientific computing/numerical analysis course. Graduate students in various data mining and pattern recognition areas who need an introduction to linear algebra techniques will also find the book useful.
ISBN: 9780898716269 (pbk.) :NT2599
LCCN: 2006041348Subjects--Topical Terms:
528622
Data mining.
LC Class. No.: QA76.9.D343 / E52 2007
Dewey Class. No.: 005.74
Matrix methods in data mining and pattern recognition /
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Matrix methods in data mining and pattern recognition /
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Includes bibliographical references (p. 209-216) and index.
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I: Linear algebra concepts and matrix decompositions -- 1: Vectors and matrices in data mining and pattern recognition -- 2: Vectors and matrices -- 3: Linear systems and least squares -- 4: Orthogonality -- 5: QR decomposition -- 6: Singular value decomposition -- 7: Reduced-rank least squares models -- 8: Tensor decomposition -- II: Data mining applications -- 10: Classification of handwritten digits -- 11: Text mining -- 12: Page ranking for a Web search engine -- 13: Automatic key word and key sentence extraction -- 14: Face recognition using tensor SVD -- III: Computing the matrix decompositions -- 15: Computing eigenvalues and singular values.
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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. The book is intended for undergraduate students who have previously taken an introductory scientific computing/numerical analysis course. Graduate students in various data mining and pattern recognition areas who need an introduction to linear algebra techniques will also find the book useful.
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