Database Reference
In-Depth Information
[PCTM02]
Poole, J., Chang, D.T., Tolbert, D., Mellor, D.: Common Warehouse Metamodel.
An Introduction to the Standard for Data Warehouse Integration. Wiley, New York
(2002)
[PCTM03]
Poole, J., Chang, D.T., Tolbert, D., Mellor, D.: Common Warehouse Metamodel.
Developer's Guide. Wiley, New York (2003)
[Pfl10]
Pfl¨ger D.: Spatial adaptive sparse grids for high-dimensional problems. Disserta-
tion, TU M¨nchen (2010)
[PR98]
Parr, R., Russel, S.J.: Reinforcement learning with hierarchies of machines. Adv.
Neural Inf. Process. Syst., 11, 1088-1095 (1998)
[PMML]
http://www.dmg.org
[Pek77]
Pekelis V.: Kleine Enzyklop¨die von der großen Kybernetik (in German).
Kinderbuchverlag, Berlin (Ost) (1977)
[Pia04]
Pias C.: Zeit der Kybernetik - Eine Einstimmung (in German). In: Cyberntics |
Kybernetik. The Macy-Conferences 1946-1953. Band 2. diaphanes, Z¨rich/Berlin
(2004)
[RFP10]
Recht, B., Fazel, M., Parrilo, P.A.: Guaranteed minimum rank solutions of matrix
equations via nuclear norm minimization. SIAM Rev. 52(3), 471-501 (2010)
[RFST10]
Rendle, S., Freudenthaler, C., Schmidt-Thieme L.: Factorizing personalized Markov
chains for next-basket recommendation. In: Proceedings of the 19th International
World Wide Web Conference (WWW 2010), ACM. Raleigh, NC, USA (2010)
[Rip94]
Ripley, B.D.: Neural networks and related methods for classification. J. R. Stat. Soc.
B 56(3), 409-456 (1994)
[RN02]
Russell, S.J., Norvig, P.: Artificial Intelligence: A Modern Approach. Prentice Hall,
Englewood Cliffs (2002)
[RRSK11]
Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.): Recommender Systems
Handbook. Springer, Berlin (2011)
[RS05]
Rennie J.D.M., Srebro N.: Fast maximum margin matrix factorization for collabo-
rative prediction. In: Proceedings of the 22nd International Conference on Machine
Learning, pp. 713-719. ACM. New York City (2005)
[RSP05]
Rojanavasu, P., Srinil, P., Pinngern, O.: New recommendation system using rein-
forcement
learning. Proceedings of
the Fourth International Conference on
eBusiness, Bangkok, 19-20 Nov 2005
[Sal97]
Salzberg, S.L.: On comparing classifiers: pitfalls to avoid and a recommended
approach. Data Min. Knowl Discov. 1, 317-327 (1997)
[SB98]
Sutton, R.S., Barto, A.G.: Reinforcement Learning. An Introduction. MIT Press,
Cambridge/London (1998)
[Sem81]
Semjonow, N. Wissenschaft und Gesellschaft (in Russian). Nauka (1981)
[SHB05]
Shani, G., Heckerman, D., Brafman, R.I.: An MDP-based recommender system.
J. Mach. Learn. Res. 6, 1265-1295 (2005)
[Sin98]
Singh, S.: 2d spiral pattern recognition with possible measures. Pattern Recognit.
Lett. 19(2), 141-147 (1998)
[SKKR00]
Sarwar, B., Karypis, G., Konstan, J., Riedl J.: Analysis of recommendation algo-
rithms for e-commerce. EC'00, Minneapolis, 17-20 Oct 2000
[Smo63]
Smolyak, S.A.: Quadrature and interpolation formulas for tensor products of certain
classes of functions. Dokl. Akad. Nauk SSSR. 148, 1042-1043 (1963). Russian,
Engl. Transl.: Soviet Math. Dokl. 4, 240-243 (1963)
[SO11]
Savostyanov, D., Oseledets, I.: Fast adaptive interpolation of multi-dimensional
arrays in tensor train format. In: Proceedings of 7th International Workshop on
Multidimensional Systems (nDS), IEEE. Poitiers, France (2011)
[SPS99]
Sutton, R., Precup, D., Singh, S.: Between MDPs and semi-MDPs: a framework for
temporal abstraction in reinforcement learning. Artif. Intell. 112(1), 181-211 (1999)
[StSa96]
Sutton R.S., Santamaria J.C.: A standard interface for reinforcement learning soft-
ware in C++. http://envy.cs.umass.edu/People/sutton/RLinterface/RLinterface.html
Search WWH ::




Custom Search