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Matrix methods in data mining and pa...

Elden, Lars, (1944-)

Matrix methods in data mining and pattern recognition /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Matrix methods in data mining and pattern recognition // Lars Elden.
作者:	Elden, Lars,
出版者:	Philadelphia, PA :Society for Industrial and Applied Mathematics, : c2007.,
面頁冊數:	x, 224 p. :ill. ; : 26 cm.;
標題:	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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100 1 $a Elden, Lars, $d 1944- $3 873427
245 1 0 $a Matrix methods in data mining and pattern recognition / $c Lars Elden.
260 # $a Philadelphia, PA : $b Society for Industrial and Applied Mathematics, $c c2007.
300 $a x, 224 p. : $b ill. ; $c 26 cm.
490 1 $a Fundamentals of algorithms
504 $a Includes bibliographical references (p. 209-216) and index.
505 0 # $a 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.
520 # $a 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.
650 # 0 $a Data mining. $3 528622
650 # 0 $a Pattern recognition systems $x Mathematical models. $3 744224
650 # 0 $a Algebras, Linear. $3 528115
830 0 $a Fundamentals of algorithms. $3 744223