Sprache: Englisch
Verlag: Cambridge University Press, 2009
ISBN 10: 0521864674 ISBN 13: 9780521864671
Anbieter: World of Books (was SecondSale), Montgomery, IL, USA
Zustand: Good. Item in good condition. Textbooks may not include supplemental items i.e. CDs, access codes etc.
Sprache: Englisch
Verlag: Cambridge University Press, 2009
ISBN 10: 0521864674 ISBN 13: 9780521864671
Anbieter: Books From California, Simi Valley, CA, USA
hardcover. Zustand: Good.
Sprache: Englisch
Verlag: Cambridge University Press, 2009
ISBN 10: 0521864674 ISBN 13: 9780521864671
Anbieter: Books From California, Simi Valley, CA, USA
hardcover. Zustand: Very Good.
Sprache: Englisch
Verlag: Cambridge University Press, 2009
ISBN 10: 0521864674 ISBN 13: 9780521864671
Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes Königreich
EUR 102,17
Anzahl: Mehr als 20 verfügbar
In den WarenkorbZustand: New. In.
Sprache: Englisch
Verlag: Cambridge University Press, 2009
ISBN 10: 0521864674 ISBN 13: 9780521864671
Anbieter: Kennys Bookstore, Olney, MD, USA
EUR 145,34
Anzahl: Mehr als 20 verfügbar
In den WarenkorbZustand: New. Sure to be influential, this book lays the foundations for the use of algebraic geometry in statistical learning theory. Series: Cambridge Monographs on Applied and Computational Mathematics. Num Pages: 300 pages, 13 b/w illus. BIC Classification: PBMW; UYQM. Category: (UU) Undergraduate. Dimension: 237 x 159 x 21. Weight in Grams: 540. . 2009. Illustrated. hardcover. . . . . Books ship from the US and Ireland.
Sprache: Englisch
Verlag: Cambridge University Press, 2009
ISBN 10: 0521864674 ISBN 13: 9780521864671
Anbieter: Revaluation Books, Exeter, Vereinigtes Königreich
EUR 144,27
Anzahl: 2 verfügbar
In den WarenkorbHardcover. Zustand: Brand New. 1st edition. 300 pages. 9.00x6.25x1.00 inches. In Stock.
Sprache: Englisch
Verlag: Cambridge University Press, 2009
ISBN 10: 0521864674 ISBN 13: 9780521864671
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Sure to be influential, this book lays the foundations for the use of algebraic geometry in statistical learning theory. Many widely used statistical models and learning machines applied to information science have a parameter space that is singular: mixture models, neural networks, HMMs, Bayesian networks, and stochastic context-free grammars are major examples. Algebraic geometry and singularity theory provide the necessary tools for studying such non-smooth models. Four main formulas are established: 1. the log likelihood function can be given a common standard form using resolution of singularities, even applied to more complex models; 2. the asymptotic behaviour of the marginal likelihood or 'the evidence' is derived based on zeta function theory; 3. new methods are derived to estimate the generalization errors in Bayes and Gibbs estimations from training errors; 4. the generalization errors of maximum likelihood and a posteriori methods are clarified by empirical process theory on algebraic varieties.