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[BT96] Bertsekas, D.P., Tsitsiklis, J.N.: Neuro-Dynamic Programming. Athena Scientific,
Belmont (1996)
[Bun92] Bungartz, H.-J: D¨nne Gitter und deren Anwendung bei der adaptiven L¨sung der
dreidimensionalen Poisson-Gleichung (in German). Dissertation, TU M¨nchen
(1992)
[BGRZ94] Bungartz, H.-J., Griebel, M., R¨schke, D., Zenger, C.: Pointwise convergence of the
combination technique for Laplace's equation. East-west, J. Numer. Math. 2, 21-45
(1994)
[CR08] Cande's, E.J., Recht, B.: Exact matrix completion via convex optimization. Found.
Comput. Math. 9, 717-772 (2008)
[CRT06] Cand ` s, E.J., Romberg, J., Tao, T.: Stable signal recovery from incomplete and
inaccurate measurements. Comm. Pure Appl. Math. 59, 1207-1223 (2006)
[CS09] Chen, J., Saad, Y.: Lanczos vectors versus singular vectors for effective dimension
reduction. IEEE Trans. Knowl. Data Eng. 21, 1091-1103 (2009)
[CT10] Cand ` s, E.J., Tao, T.: The power of convex relaxation: near-optimal matrix com-
pletion. IEEE Trans. Info. Theor. 56(5), 2053-2080 (2010)
[Dau92] Daubechies, I.: Ten Lectures on Wavelets. Society for Industrial and Applied
Mathematics, Philadelphia (1992)
[DHS05] Ding, C., He, X., Simon, H.D.: On the equivalence of nonnegative matrix factori-
zation and spectral clustering. Proc. SIAM Data Mining Conf. 4, 606-610 (2005)
[Diet98] Dietterich, T.G.: Approximate statistical tests for comparing supervised classifica-
tion learning algorithms. Neural Comput. 10(7), 1895-1924 (1998)
[Diet00] Dietterich, T.G.: Hierarchical reinforcement learning with the MAXQ value func-
tion decomposition. J. Artif. Intell. Res. 13, 227-303 (2000)
[DLDMV00] De Lathauwer, L., De Moor, B., Vandewalle, J.: A multilinear singular value
decomposition. SIAM J. Matrix Anal. Appl. 21(4), 1253-1278 (2000)
[DLJ10]
Ding, C., Li, T., Jordan, M.I.: Convex and semi-nonnegative matrix factorizations.
IEEE Trans. Pattern Anal. Mach. Intell. 32(1), 45-55 (2010)
[DMC11]
http://www.data-mining-cup.de/en/review/dmc-2011/
[Don06]
Donoho, D.L.: Compressed sensing. IEEE Trans. Info.Theor. 52(4), 1289-1306
(2006)
[DSL08]
De Silva, V., Lim, L.-H.: Tensor rank and the ill-posedness of the best low-rank
approximation problem. SIAM J. Matrix Anal. Appl. 30(3), 1084-1127 (2008)
[EPP00]
Evgeniou, T., Pontil, M., Poggio, T.: Regularization networks and support vector
machines. Adv. Comput. Math. 13, 1-50 (2000)
Faber, G.: ¨ ber stetige Funktionen (in German). Math. Annal. 66, 81-94 (1909)
[Fab9]
[Fed64]
Fedorenko, R.P.: The speed of convergence of one iterative process. USSR Comput.
Math. Math. Phys. 4, 227-235 (1964)
[FM 01]
Fung, G., Mangasarian, O.L.: Proximity support vector machine classifiers. In:
Provost, F., Srikant, R. (eds.) Proceedings of the Seventh ACM SIGKDD Interna-
tional Conference on Knowledge Discovery and Data Mining, pp. 77-86 (2001)
[Fun06]
Funk, S.: Netflix update: try this at home. http://sifter.org/~simon/journal/20061211.
html
[Gar06]
Garcke J.: Regression with the optimized combination technique. In: ICML '06:
Proceedings of the 23th International Conference on Machine learning, pp. 321-328.
ACM Press, New York (2006)
[Gar11]
Garcke, J.: A dimension adaptive sparse grid combination technique for machine
learning. In: Read, W., Larson, J.W., Roberts, A.J. (eds.) Proceedings of the 13th
Biennial Computational Techniques and Application Conference, CTAC-2006, vol.
48 of ANZIAM J., pp. C725-C740 (2007)
[Gar12b]
Garcke, J.: A dimension adaptive combination technique using localized adaptation
criteria. In: Bock, H.G., Hoang, X.P., Rannacher, R., Schl¨der, J.P. (eds.) Modeling,
Simulation and Optimization of Complex Processes, pp. 115-125. Springer, Dor-
drecht (2012)
 
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