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Sra, S.; Nowozin, S.; Wright, S. J. (Hg.): Optimization for Machine Learning. MIT Press, Cambridge, MA, USA (2011), 494 S.
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Lu, H. H.-S.; Zhao, H. (Hg.): Handbook of Statistical Bioinformatics. Springer, Berlin, Germany (2011), IX,627 S.

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Schmidt, M.; Kim, D.; Sra, S.: Projected Newton-type methods in machine learning. In: Optimization for Machine Learning, S. 305 - 330 (Hg. Sra, S.; Nowozin, S.; Wright, S. J.). MIT Press, Cambridge, MA, USA (2011)
264.
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von Luxburg, U.; Schölkopf, B.: Statistical Learning Theory: Models, Concepts, and Results. In: Handbook of the History of Logic Vol. 10: Inductive Logic, S. 651 - 706 (Hg. Gabbay; M., D.; Woods; J.; Hartmann et al.). Elsevier North Holland, Amsterdam, Netherlands (2011)
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Andriluka, M.; Sigal, L.; Black, M. J.: Benchmark datasets for pose estimation and tracking. In: Visual Analysis of Humans: Looking at People, S. 253 - 274 (Hg. Moeslund, T. B.; Hilton, A.; Krüger, V.; Sigal, L.). Springer, London (2011)
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Borgwardt, K. M.: Kernel Methods in Bioinformatics. In: Handbook of Statistical Bioinformatics, S. 317 - 33 (Hg. Lu Schölkopf B., H. H.-S.; Zhao, H.). Springer, Berlin (2011)
267.
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Charpiat, G.; Bezrukov, I.; Hofmann, M.; Altun, Y.; Schölkopf, B.: Machine Learning Methods for Automatic Image Colorization. In: Computational Photography: Methods andApplications, S. 395 - 418 (Hg. Lukac, R.). CRC Press, Boca Raton, FL, USA (2011)
268.
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Kudera, S.; Maus, L.; Zanella, M.; Parak, W.: Core–Shell Nanocrystals. In: Comprehensive Nanoscience and Technology. Vol. 1, S. 272 - 287 (Hg. Andrews, D.L.; Scholes, G.D.; Wiederrecht, G.P.). Elsevier B.V. (2011)
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Peters, J.; Tedrake, R.; Roy, N.; Morimoto, J.: Robot Learning. In: Encyclopedia of Machine Learning, S. 865 - 869 (Hg. Sammut; C.; Webb; I., G.). Springer, New York, NY, USA (2011)
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Roth, S.; Black, M. J.: Field of experts. In: Markov Random Fields for Vision and Image Processing, S. 297 - 310 (Hg. Blake, A.; Kohli, P.; Rother, C.). MIT Press, Cambridge, Mass. [et al.] (2011)
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Roth, S.; Black, M. J.: Steerable random fields for image restoration and inpainting. In: Markov Random Fields for Vision and Image Processing, S. 377 - 387 (Hg. Blake, A.; Kohli, P.; Rother, C.). MIT Press, Cambridge, Mass. [et al.] (2011)

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Fuess, H.; Scardi, P.; Welzel, U. (Hg.): Proceedings of the Twelfth European Powder Diffraction Conference (Zeitschrift für Kristallographie Proceedings, 1). Twelfth European Powder Diffraction Conference (EPDIC 12), Darmstadt, 27. August 2010 - 30. August 2010. Odenbourg Wissenschaftverlag, München (2011), 492 S.
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Kakade, S. M.; von Luxburg, U. (Hg.): COLT 2011, 24th Annual Conference on Learning Theory- (JMLR: Workshop and Conference Proceedings, Vol. 19). COLT 2011. 24th Annual Conference on Learning Theory, Budapest [Hungary], 09. Juli 2011 - 11. Juli 2011. MIT Press, Cambridge, MA (2011), 834 S.

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Balduzzi, D.: Information, learning and falsification. In: NIPS 2011 Philosophy and Machine Learning Workshop, S. 1 - 4. NIPS 2011 Philosophy and Machine Learning Workshop, Sierra Nevada, 17. Dezember 2011. (2011)
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Del Favero, S.; Varagnolo, D.; Dinuzzo, F.; Schenato, L.; Pillonetto, G.: On the discardability of data in Support Vector Classification problems. In: 50th IEEE Conference on Decision and Control and European Control Conference (CDC - ECC 2011), S. 3210 - 3215. (2011)
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Langovoy, M.; Sra, S.: Statistical estimation for optimization problems on graphs. In: NIPS Workshop on Discrete Optimization in Machine Learning (DISCML) 2011: Uncertainty, Generalization and Feedback, S. 1 - 6. (2011)
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Dinuzzo, F.; Fukumizu, K.: Learning low-rank output kernels. 3rd Asian Conference on Machine Learning (ACML 2011), Taoyuan, Taiwan. JMLR: Workshop and Conference Proceedings 20, S. 181 - 196 (2011)
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Hirsch, M.; Schuler, C. J.; Harmeling, S.; Schölkopf, B.: Fast removal of non-uniform camera shake. In: 13th IEEE International Conference on Computer Vision (ICCV 2011), S. 463 - 470. (2011)
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Schuler, C. J.; Hirsch, M.; Harmeling, S.; Schölkopf, B.: Non-stationary correction of optical aberrations. In: 13th IEEE International Conference on Computer Vision (ICCV 2011), S. 659 - 666. (2011)
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Zhang, K.; Hyvärinen, A.: A general linear non-Gaussian state-space model: Identifiability, identification, and applications. In: 3rd Asian Conference on Machine Learning (ACML 2011), S. 113 - 128. (2011)
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