Sprache: Englisch
Verlag: Cambridge University Press, 2013
ISBN 10: 0521887933 ISBN 13: 9780521887939
Anbieter: PBShop.store UK, Fairford, GLOS, Vereinigtes Königreich
EUR 98,62
Anzahl: 3 verfügbar
In den WarenkorbHRD. Zustand: New. New Book. Shipped from UK. Established seller since 2000.
Anbieter: Revaluation Books, Exeter, Vereinigtes Königreich
EUR 90,28
Anzahl: 1 verfügbar
In den WarenkorbHardcover. Zustand: Brand New. 504 pages. 10.50x7.50x1.00 inches. In Stock.
Sprache: Englisch
Verlag: Cambridge University Press, 2014
ISBN 10: 0521887933 ISBN 13: 9780521887939
Anbieter: moluna, Greven, Deutschland
Zustand: New. Big data poses challenges that require both classical multivariate methods and modern machine-learning techniques. This coherent treatment integrates theory with data analysis, visualisation and interpretation of the analysis. Problems, data sets and MATL.
Sprache: Englisch
Verlag: Cambridge University Press Dez 2013, 2013
ISBN 10: 0521887933 ISBN 13: 9780521887939
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Buch. Zustand: Neu. Neuware - 'Big data' poses challenges that require both classical multivariate methods and contemporary techniques from machine learning and engineering. This modern text equips you for the new world - integrating the old and the new, fusing theory and practice and bridging the gap to statistical learning. The theoretical framework includes formal statements that set out clearly the guaranteed 'safe operating zone' for the methods and allow you to assess whether data is in the zone, or near enough. Extensive examples showcase the strengths and limitations of different methods with small classical data, data from medicine, biology, marketing and finance, high-dimensional data from bioinformatics, functional data from proteomics, and simulated data. High-dimension low-sample-size data gets special attention. Several data sets are revisited repeatedly to allow comparison of methods. Generous use of colour, algorithms, Matlab code, and problem sets complete the package. Suitable for master's/graduate students in statistics and researchers in data-rich disciplines.