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J. Comput. Commun. Control 8(1), 30–36 (2013) 33. : A new local distance-based outlier detection approach for scattered real-word data. Adv. Knowl. Discov. Data Min. 5476, 813–822 (2009) 34. : Combining nearest neighbor data description and structural risk minimization for one-class classification. Neural Comput. Appl. 18(2), 175–183 (2009) 35. : Mining distance-based outlier sinnear linear time with randomization, a simplep runing rule. In: Proceedings of the 9th ACM International Conference on Knowl edge Discovery and Data Mining (SIGKDD), pp.

Graph. 34(4), 98:1–98:10 (2015). 1145/2766959 4. : Model compression. In: Proceedings of the Twelfth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Philadelphia, PA, USA, 20–23 Aug 2006, pp. 535–541 (2006) 5. : Bayesian face revisited: a joint formulation. , Schmid, C. ) ECCV 2012, Part III. LNCS, vol. 7574, pp. 566–579. Springer, Heidelberg (2012). 1007/978-3-642-33712-3 41 6. : Learning a similarity metric discriminatively, with application to face verification. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol.

R. Stat. Soc. Ser. B 58(1), 267–288 (1996) 19. : Adapting to unknown smoothness via wavelet shrinkage. J. Am. Stat. Assoc. 90(432), 1200–1224 (1995) 20. : Pathwise coordinate optimization. Ann. Appl. Stat. 1(2), 302–332 (2007) 21. : Self-taught learning: transfer learning from unlabeled data. In: Proceedings of the 24th International Conference on Machine Learning (ICML), pp. com Abstract. In this work we have investigated face verification based on deep representations from Convolutional Neural Networks (CNNs) to find an accurate and compact face descriptor trained only on a restricted amount of face image data.

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