Lingua: Inglese
Editore: Cambridge University Press, 2013
ISBN 10: 0521887933 ISBN 13: 9780521887939
Da: PBShop.store UK, Fairford, GLOS, Regno Unito
EUR 89,30
Quantità: 3 disponibili
Aggiungi al carrelloHRD. Condizione: New. New Book. Shipped from UK. Established seller since 2000.
Da: Revaluation Books, Exeter, Regno Unito
EUR 89,86
Quantità: 1 disponibili
Aggiungi al carrelloHardcover. Condizione: Brand New. 504 pages. 10.50x7.50x1.00 inches. In Stock.
Lingua: Inglese
Editore: Cambridge University Press, 2014
ISBN 10: 0521887933 ISBN 13: 9780521887939
Da: moluna, Greven, Germania
EUR 81,33
Quantità: 3 disponibili
Aggiungi al carrelloCondizione: 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.
Lingua: Inglese
Editore: Cambridge University Press Dez 2013, 2013
ISBN 10: 0521887933 ISBN 13: 9780521887939
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 102,49
Quantità: 2 disponibili
Aggiungi al carrelloBuch. Condizione: 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.