Học Đo Lường Khoảng Cách Cho Máy Vector Hỗ Trợ: Một Tiếp Cận Học Nhiều Đầu Kernel

Aiolli F, Donini M (2015) Easymkl: a scalable multiple kernel learning algorithm. Neurocomputing 169:215–224 Bach FR (2009) Exploring large feature spaces with hierarchical multiple kernel learning. In: Advances in neural information processing systems, pp 105–112 Bach FR, Lanckriet GR, Jordan MI (2004) Multiple kernel learning, conic duality, and the smo algorithm. In: Proceedings of the twenty-first international conference on Machine learning, ACM, p 6 Boiman O, Shechtman E, Irani M (2008) In defense of nearest-neighbor based image classification. In: IEEE Conference on computer vision and pattern recognition, 2008. CVPR 2008, pp 1–8 Cao Q, Ying Y, Li P (2013) Similarity metric learning for face recognition. In: IEEE international conference on computer vision, pp 2408–2415 Cortes C, Mohri M, Rostamizadeh A (2009) Learning non-linear combinations of kernels. In: Advances in neural information processing systems, pp 396–404 Davis JV, Kulis B, Jain P, Sra S, Dhillon IS (2007) Information-theoretic metric learning. In: Machine learning, proceedings of the twenty-fourth international conference, pp 209–216 Demšar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7(1):1–30 Deng C, Tang X, Yan J, Liu W, Gao X (2016) Discriminative dictionary learning with common label alignment for cross-modal retrieval. IEEE Trans Multimed 18(2):208–218 Deng C, Chen Z, Liu X, Gao X, Tao D (2018) Triplet-based deep hashing network for cross-modal retrieval. IEEE Trans Image Process 27(8):3893–3903 Do H, Kalousis A (2013) Convex formulations of radius-margin based support vector machines. In: Proceedings of the 30th international conference on Machine learning, pp 169–177 Do H, Kalousis A, Wang J, Woznica A (2012) A metric learning perspective of SVM: on the relation of SVM and LMNN. Eprint Arxiv pp 308–317 Dong Y, Du B, Zhang L, Zhang L, Tao D (2017) Lam3l: locally adaptive maximum margin metric learning for visual data classification. Neurocomputing 235:1–9 Frank A, Asuncion A (2010) Uci machine learning repository [http://archive.ics.uci.edu/ml]. irvine, ca: University of california. School of Information and Computer Science 213 Gai K, Chen G, Zhang C (2010) Learning kernels with radiuses of minimum enclosing balls. In: Advances in neural information processing systems, pp 649–657 Gao X, Hoi SCH, Zhang Y, Wan J, Li J (2014) Soml: sparse online metric learning with application to image retrieval. In: Twenty-eighth AAAI conference on artificial intelligence, pp 1206–1212 Goldberger J, Roweis ST, Hinton GE, Salakhutdinov R (2004) Neighbourhood components analysis. Adv Neural Inf Process Syst 83(6):513–520 Gu Y, Wang C, You D, Zhang Y, Wang S, Zhang Y (2012) Representative multiple kernel learning for classification in hyperspectral imagery. Neurocomputing 50:215–224 Guillaumin M, Verbeek J, Schmid C (2009) Is that you? Metric learning approaches for face identification. In: 2009 IEEE 12th international conference on computer vision. IEEE, pp 498–505 Hasan MA, Ahmad S, Molla MK (2017) Protein subcellular localization prediction using multiple kernel learning based support vector machine. Mol Biosyst 13(4):785 Hoi SCH, Liu W, Lyu MR, Ma WY (2006) Learning distance metrics with contextual constraints for image retrieval. In: IEEE conference on computer vision and pattern recognition, pp 2072–2078 Jain A, Vishwanathan SVN, Varma M (2012) Spg-gmkl: generalized multiple kernel learning with a million kernels. In: ACM SIGKDD international conference on knowledge discovery and data mining, pp 750–758 Kedem D, Tyree S, Weinberger KQ, Sha F, Lanckriet G (2012) Non-linear metric learning. In: Advances in neural information processing systems, pp 2573–2581 Kloft M, Brefeld U, Sonnenburg S, Zien A (2011) Lp-norm multiple kernel learning. J Mach Learn Res 12:953–997 Lanckriet GRG, Cristianini N, Bartlett P, El Ghaoui L, Jordan MI (2002) Learning the kernel matrix with semi-definite programming. J Mach Learn Res 5(1):323–330 Lauriola I, Polato M, Aiolli F (2017) Radius-margin ratio optimization for dot-product Boolean kernel learning. In: International conference on artificial neural networks, pp 183–191 Lim DKH, Mcfee B, Lanckriet G (2013) Robust structural metric learning. In: International conference on machine learning, pp 615–623 Lu X, Wang Y, Zhou X, Ling Z (2015) A method for metric learning with multiple-kernel embedding. Neural Process Lett 43(3):923–924 Mcfee B, Lanckriet G (2011) Learning multi-modal similarity. J Mach Learn Res 12(8):491–523 Nguyen B, Morell C, De Baets B (2016) Large-scale distance metric learning for k-nearest neighbors regression. Neurocomputing 214:805–814 Nguyen N, Guo Y (2008) Metric learning: a support vector approach. In: Joint European conference on machine learning and knowledge discovery in databases, Springer, pp 125–136 Rakotomamonjy A, Bach FR, Canu S, Grandvalet Y (2008) Simplemkl. J Mach Learn Res 9(11):2491–2521 Schölkopf B, Smola A (2001) Learning with kernels: support vector machines, regularization, optimization, and beyond. MIT Press, Cambridge Shawe-Taylor J, Cristianini N (2006) Kernel methods for pattern analysis. J Am Stat Assoc 101(12):1730–1730 Sonnenburg S, Rätsch G, Schäfer C, Schölkopf B (2006) Large scale multiple kernel learning. J Mach Learn Res 7(7):1531–1565 Squarcina L, Castellani U, Bellani M, Perlini C, Lasalvia A, Dusi N, Bonetto C, Cristofalo D, Tosato S, Rambaldelli G (2017) Classification of first-episode psychosis in a large cohort of patients using support vector machine and multiple kernel learning techniques. Neuroimage 145:238–245 Torresani L, Kc L (2007) Large margin component analysis. Adv Neural Inf Process Syst 19:1385 Tran D, Sorokin A (2008) Human activity recognition with metric learning. In: European conference on computer vision, pp 548–561 Vapnik V, Chapelle O (2000) Bounds on error expectation for support vector machines. Neural Comput 12(9):2013–2036 Varma M, Babu BR (2009) More generality in efficient multiple kernel learning. In: International conference on machine learning, pp 1065–1072 Wang F, Zuo W, Zhang L, Meng D, Zhang D (2015) A kernel classification framework for metric learning. IEEE Trans Neural Netw Learn Syst 26(9):1950–1962 Wang J, Do HT, Woznica A, Kalousis A (2011) Metric learning with multiple kernels. In: Advances in neural information processing systems, pp 1170–1178 Wang J, Deng Z, Choi KS, Jiang Y, Luo X, Chung FL, Wang S (2016) Distance metric learning for soft subspace clustering in composite kernel space. Pattern Recognit 52:113–134 Weinberger KQ, Saul LK (2006) Distance metric learning for large margin nearest neighbor classification. J Mach Learn Res 10(1):207–244 Wu H, He L (2015) Combining visual and textual features for medical image modality classification with lp-norm multiple kernel learning. Neurocomputing 147(1):387–394 Xu X, Tsang IW, Xu D (2013) Soft margin multiple kernel learning. IEEE Trans Neural Netw Learn Syst 24(5):749–761 Xu Z, Jin R, King I, Lyu M (2009) An extended level method for efficient multiple kernel learning. In: Advances in neural information processing systems, pp 1825–1832 Xu Z, Weinberger KQ, Chapelle O (2012) Distance metric learning for kernel machines. arXiv preprint arXiv:1208.3422 Yang E, Deng C, Li C, Liu W, Li J, Tao D (2018) Shared predictive cross-modal deep quantization. IEEE Trans Neural Netw Learn Syst 99:1–12 Yi S, Jiang N, Wang X, Liu W (2016) Individual adaptive metric learning for visual tracking. Neurocomputing 191:273–285 Ying Y, Li P (2012) Distance metric learning with eigenvalue optimization. J Mach Learn Res 13(1):1–26 Zhang X, Mahoor MH, Mavadati SM (2015) Facial expression recognition using lp-norm MKL multiclass-SVM. Mach Vis Appl 26(4):467–483 Zhao C, Chen Y, Wei Z, Miao D, Gu X (2018) Qrkiss: a two-stage metric learning via QR-decomposition and kiss for person re-identification. Neural Process Lett 2:1–24