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Fast learning from non-i.i.d. observations

Title data

Steinwart, Ingo ; Christmann, Andreas:
Fast learning from non-i.i.d. observations.
In: Bengio, Yoshua ; Schuurmans, D. ; Lafferty, J. D. ; Williams, C. K. I. ; Culotta, A. (ed.): Advances in Neural Information Processing Systems 22. Volume 3. - Red Hook, NY : Curran , 2009 . - pp. 1768-1776

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Item Type: Article in a book
Refereed: Yes
Keywords: statistical machine learning; rate of convergence; learning rate; non-i.i.d.;
dependent data; alpha mixing; kernel methods
Institutions of the University: Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Mathematics > Chair Mathematics VII - Stochastics
Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Mathematics > Chair Mathematics VII - Stochastics > Chair Mathematics VII - Stochastics - Univ.-Prof. Dr. Andreas Christmann
Faculties
Faculties > Faculty of Mathematics, Physics und Computer Science
Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Mathematics
Result of work at the UBT: Yes
DDC Subjects: 000 Computer Science, information, general works > 004 Computer science
300 Social sciences > 310 Statistics
500 Science > 510 Mathematics
Date Deposited: 19 Oct 2015 07:29
Last Modified: 19 Oct 2015 07:29
URI: https://eref.uni-bayreuth.de/id/eprint/20526