Database Reference
In-Depth Information
[GG01a] Garcke, J., Griebel, M.: Data Mining with sparse grids using simplicial basis
functions. In: Proceedings of the Seventh ACM SIGKDD International Conference
on Knowledge Discovery and Data Mining. San Francisco, California, USA (2001)
[GGT01] Garcke, J., Griebel, M., Thess, M.: Data mining with sparse grids. Computing 67(3),
225-253 (2001)
[Gea30] Geary, R.C.: The frequency distribution of the quotient of two normal variates. J. R.
Stat. Soc. 93(3), 442-446 (1930)
[GG98] Gerstner, T., Griebel, M.: Numerical integration using sparse grids. Numer. Algo-
rithms 18, 209-232 (1998)
[Gir98] Girosi, F.: An equivalence between sparse approximation and support vector
machines. Neural Comput. 10, 1455-1480 (1998)
[GJP93] Girosi, F., Jones, M., Poggio, T.: Priors, stabilizers and basis functions: from
regularization to radial, tensor and additive splines. AI Memo No. 1430. Artificial
Intelligence Laboratory, MIT (1993)
[GJP95] Girosi, F., Jones, M., Poggio, T.: Regularization theory and neural networks archi-
tectures. Neural Comput. 7, 219-265 (1995)
[GNBT92] Goldberg, D., Nichols, D., Oki, B.M., Terry, D.: Using collaborative filtering to
weave an information tapestry. Commun. ACM 35(12), 61-70 (1992). Special issue
on information filtering
[GR04]
Golovin, N., Rahm, E.: Reinforcement Learning Architecture for Web Recommen-
dations. Proc. ITCC2004, IEEE (2004)
[GV81]
Glushkov, V.M., Vallakh, V.I.: What is OGAS? (in Russian). Kvant, Nauka (1981)
[GVL96]
Goloub, G.H., Van Loan, C.F.: Matrix Computations, 3rd edn. The Johns Hopkins
University Press, Baltimore/London (1996)
[Gra10]
Grasedyck, L.: Hierarchical singular value decomposition of
tensors. SIAM
J. Matrix Anal. Appl. 31, 2029-2054 (2010)
[GH03]
Grasedyck, L., Hackbusch, W.: Construction and arithmetics of H-matrices. Com-
puting 70, 295-334 (2003)
[Gri92]
Griebel, M.: The combination technique for the sparse grid solution of PDEs on
multiprocessor machines. Parallel Process. Lett. 2(1), 61-70 (1992)
[GSZ92]
Griebel, M., Schneider, M., Zenger, C.: A combination technique for the solution of
sparse grid problems. In: de Groen, P., Beauwens, R. (eds.) Iterative Methods in
Linear Algebra, pp. 263-281. Elsevier, Amsterdam (1992)
[GE94]
Gu, M., Eisenstat, S.C.: A stable and efficient algorithm for the rank-one modifica-
tion of the symmetric eigenproblem. SIAM J. Matrix Anal. Appl. 15, 1266-1276
(1994)
[Ha85]
Hackbusch, W.: Multigrid Methods and Applications. Springer, Berlin (1985)
[Ha99]
Hackbusch, W.: A sparse matrix arithmetic based on H-matrices. Part I: introduction
to H-matrices. Computing 62, 89-108 (1999)
[HK00]
Hackbusch, W., Khoromskij, B.N.: A sparse H-matrix arithmetic. Part II: applica-
tion to multi-dimensional problems. Computing 64, 21-47 (2000)
[HK09]
Hackbusch, W., K¨hn, S.: A new scheme for the tensor representation. J. Fourier
Anal. Appl. 15, 706-722 (2009)
[Heg03]
Hegland, M.: Adaptive sparse grids. In: Burrage, K., Sidje, R.B. (eds.) Proceedings
of 10th Computational Techniques and Application Conference CTAC-2001, vol.
44, pp. C335-C353. Brisbane, Australia (2003)
[Hol92]
Holland, J.H.: Adaptation in Natural and Artificial Systems: An Introductory Anal-
ysis with Applications to Biology, Control, and Artificial Intelligence. The MIT
Press, Cambridge (1992)
[HJ85]
Horn, R.A., Johnson, C.R.: Matrix Analysis. Cambridge University Press, New York
(1985)
[HL92]
Hoschek, J., Lasser, D.: Grundlagen der geometrischen Datenverarbeitung. Teubner,
Stuttgart (1992). Chapter 9 (in German)
