9783540737490 - principal manifolds for data visualization and dimension reduction: 58 (10 risultati)
Lingua: Inglese
Editore: Springer, 2007
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Principal Manifolds for Data Visualization and Dimension Reduction (Lecture Notes in Computational Science and Engineering, 58)
Gorban, Alexander N.; Kégl, Balázs; Wunsch, Donald C.; Zinovyev, Andrei
Lingua: Inglese
Editore: Springer (edition 2008), 2007
Serie: Libro 35 di 111 - Lecture Notes in Computational Science and Engineering
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Paperback. Condizione: Very Good. 2008. It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting.
Lingua: Inglese
Editore: Springer, 2007
Serie: Libro 35 di 111 - Lecture Notes in Computational Science and Engineering
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Condizione: New. In.
Altre immaginiLingua: Inglese
Editore: Springer, 2007
Serie: Libro 35 di 111 - Lecture Notes in Computational Science and Engineering
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Taschenbuch. Condizione: Neu. Principal Manifolds for Data Visualization and Dimension Reduction | Alexander N. Gorban (u. a.) | Taschenbuch | Lecture Notes in Computational Science and Engineering | xxiv | Englisch | 2007 | Springer | EAN 9783540737490 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17…, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Lingua: Inglese
Editore: Springer, 2007
Serie: Libro 35 di 111 - Lecture Notes in Computational Science and Engineering
- Brossura
Da: Mispah books, Redhill, SURRE, Regno UnitoMispah books
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Paperback. Condizione: Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book.
Lingua: Inglese
Editore: Springer, 2007
Serie: Libro 35 di 111 - Lecture Notes in Computational Science and Engineering
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Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH
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Taschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - In 1901, Karl Pearson invented Principal Component Analysis (PCA). Since then, PCA serves as a prototype for many other tools of data analysis, visualization and dimension reduction: Independent Component Analysis (ICA), Multidimensional Scaling (…MDS), Nonlinear PCA (NLPCA), Self Organizing Maps (SOM), etc. The book starts with the quote of the classical Pearson definition of PCA and includes reviews of various methods: NLPCA, ICA, MDS, embedding and clustering algorithms, principal manifolds and SOM. New approaches to NLPCA, principal manifolds, branching principal components and topology preserving mappings are described as well. Presentation of algorithms is supplemented by case studies, from engineering to astronomy, but mostly of biological data: analysis of microarray and metabolite data. The volume ends with a tutorial 'PCA and K-meansdecipher genome'. The book is meant to be useful for practitioners in applied data analysis in life sciences, engineering, physics and chemistry; it will also be valuable to PhD students and researchers in computer sciences, applied mathematics and statistics.
Lingua: Inglese
Editore: Springer Verlag, 2007
Serie: Libro 35 di 111 - Lecture Notes in Computational Science and Engineering
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Da: Revaluation Books, Exeter, Regno UnitoRevaluation Books
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Paperback. Condizione: Brand New. 1st edition. 334 pages. 9.00x6.00x0.50 inches. In Stock.
Lingua: Inglese
Editore: Springer Berlin Heidelberg, 2007
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Da: moluna, Greven, Germaniamoluna
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Kartoniert / Broschiert. Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. New approaches to NLPCA, principal manifolds, branching principal components and topology preserving mappings are describedPresentation of algorithms is supplemented by case studiesThe book s…tarts with the quote of the classical Pea.
Lingua: Inglese
Editore: Springer Berlin Heidelberg Okt 2007, 2007
Serie: Libro 35 di 111 - Lecture Notes in Computational Science and Engineering
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Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermaniaBuchWeltWeit Ludwig Meier e.K.
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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -In 1901, Karl Pearson invented Principal Component Analysis (PCA). Since then, PCA serves as a prototype for many other tools of data analysis, visualization and dimension reduction: Independent Component Analysis (ICA), Multidimen…sional Scaling (MDS), Nonlinear PCA (NLPCA), Self Organizing Maps (SOM), etc. The book starts with the quote of the classical Pearson definition of PCA and includes reviews of various methods: NLPCA, ICA, MDS, embedding and clustering algorithms, principal manifolds and SOM. New approaches to NLPCA, principal manifolds, branching principal components and topology preserving mappings are described as well. Presentation of algorithms is supplemented by case studies, from engineering to astronomy, but mostly of biological data: analysis of microarray and metabolite data. The volume ends with a tutorial 'PCA and K-meansdecipher genome'. The book is meant to be useful for practitioners in applied data analysis in life sciences, engineering, physics and chemistry; it will also be valuable to PhD students and researchers in computer sciences, applied mathematics and statistics. 364 pp. Englisch.
Lingua: Inglese
Editore: Springer, Springer Spektrum Okt 2007, 2007
Serie: Libro 35 di 111 - Lecture Notes in Computational Science and Engineering
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Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germaniabuchversandmimpf2000
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Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -In 1901, Karl Pearson invented Principal Component Analysis (PCA). Since then, PCA serves as a prototype for many other tools of data analysis, visualization and dimension reduction: Independent Component Analysis (ICA), Multidimension…al Scaling (MDS), Nonlinear PCA (NLPCA), Self Organizing Maps (SOM), etc. The book starts with the quote of the classical Pearson definition of PCA and includes reviews of various methods: NLPCA, ICA, MDS, embedding and clustering algorithms, principal manifolds and SOM. New approaches to NLPCA, principal manifolds, branching principal components and topology preserving mappings are described as well. Presentation of algorithms is supplemented by case studies, from engineering to astronomy, but mostly of biological data: analysis of microarray and metabolite data. The volume ends with a tutorial 'PCA and K-means decipher genome'. The book is meant to be useful for practitioners in applied data analysis in life sciences, engineering, physics and chemistry; it will also be valuable to PhD students and researchers in computer sciences, applied mathematics and statistics.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 364 pp. Englisch.
