Matrix methods data mining von elden lars (24 Ergebnisse)

Zustand: good. Book shows general signs of use and handling. May have light wear on the cover or edges and minimal writing or highlighting. Binding remains tight, and pages are clean and readable.

Paperback. Zustand: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority.

Zustand: Good. Item in good condition. Textbooks may not include supplemental items i.e. CDs, access codes etc.

Zustand: good. This copy does have curled corners.

Zustand: good. May show signs of wear, highlighting, writing, and previous use. This item may be a former library book with typical markings. No guarantee on products that contain supplements Your satisfaction is 100% guaranteed. Twenty-five year bookseller with shipments to over fifty million happy customers.…

Tapa Blanda. Zustand: Bien. Tapa blanda. Abundantes gráficos e ilustraciones.9780898716269. Siam. Estados Unidos. 2007. 26x18 centímetros. 224 páginas. Tapa blanda. Estado=Bien. Inglés.

Zustand: new.

Zustand: New.

Paperback. Zustand: New. Second Edition. This thoroughly revised second edition provides an updated treatment of numerical linear algebra techniques for solving problems in data mining and pattern recognition. Adopting an application-oriented approach, the author introduces matrix theory and decompositions, describes how modern matrix methods can be applied in real life scenarios, and provides a set of tools that students can modify for a particular application.Building on material from the first edition, the author discusses basic graph concepts and their matrix counterparts. He introduces the graph Laplacian and properties of its eigenvectors needed in spectral partitioning and describes spectral graph partitioning applied to social networks and text classification. Examples are included to help readers visualize the results. This new edition also presents matrix-based methods that underlie many of the algorithms used for big data.The book provides a solid foundation to further explore related topics and presents applications such as classification of handwritten digits, text mining, text summarization, PageRank computations related to the Google search engine, and facial recognition. Exercises and computer assignments are available on a Web page that supplements the book.Matrix Methods in Data Mining and Pattern Recognition, Second Edition is primarily for undergraduate students who have previously taken an introductory scientific computing/numerical analysis course and graduate students in data mining and pattern recognition areas who need an introduction to linear algebra techniques.…

Zustand: As New. Unread book in perfect condition.

Paperback. Zustand: Brand New. 2nd revised edition edition. 229 pages. 10.00x7.05x0.63 inches. In Stock.

Zustand: New.

Zustand: As New. Unread book in perfect condition.

Zustand: New.

Paperback / softback. Zustand: New. New copy - Usually dispatched within 4 working days.

Zustand: New. 2019. Second. paperback. . . . . . Books ship from the US and Ireland.

Zustand: New. Second edition NO-PA16APR2015-KAP.

paperback. Zustand: New. In shrink wrap. Looks like an interesting title.

Zustand: New. In English.

Zustand: good. May show signs of wear, highlighting, writing, and previous use. This item may be a former library book with typical markings. No guarantee on products that contain supplements Your satisfaction is 100% guaranteed. Twenty-five year bookseller with shipments to over fifty million happy customers.…

Zustand: New. Provides an updated treatment of numerical linear algebra techniques for solving problems in data mining and pattern recognition. Adopting an application-oriented approach, the author introduces matrix theory and decompositions, describes how modern matrix .

Taschenbuch. Zustand: Neu. Neuware.

Paperback. Zustand: New. Second Edition. This thoroughly revised second edition provides an updated treatment of numerical linear algebra techniques for solving problems in data mining and pattern recognition. Adopting an application-oriented approach, the author introduces matrix theory and decompositions, describes how modern matrix methods can be applied in real life scenarios, and provides a set of tools that students can modify for a particular application.Building on material from the first edition, the author discusses basic graph concepts and their matrix counterparts. He introduces the graph Laplacian and properties of its eigenvectors needed in spectral partitioning and describes spectral graph partitioning applied to social networks and text classification. Examples are included to help readers visualize the results. This new edition also presents matrix-based methods that underlie many of the algorithms used for big data.The book provides a solid foundation to further explore related topics and presents applications such as classification of handwritten digits, text mining, text summarization, PageRank computations related to the Google search engine, and facial recognition. Exercises and computer assignments are available on a Web page that supplements the book.Matrix Methods in Data Mining and Pattern Recognition, Second Edition is primarily for undergraduate students who have previously taken an introductory scientific computing/numerical analysis course and graduate students in data mining and pattern recognition areas who need an introduction to linear algebra techniques.…

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