[1] Chapelle O, Vapnik V, Bousquet O, et al. Choosing multiple parameters for support vector machines[J]. Machine Learning, 2002, 46(1): 131-159.
[2] Feng C, Liao S Z. Model selection for Gaussian kernel sup-port vector machines in random Fourier feature space[J]. Journal of Computer Research and Development, 2016, 53(9): 1971-1978.冯昌, 廖士中. 随机傅里叶特征空间中高斯核支持向量机模型选择[J]. 计算机研究与发展, 2016, 53(9): 1971-1978.
[3] Bartlett P L, Mendelson S. Rademacher and Gaussian com-plexities: risk bounds and structural results[J]. Journal of Machine Learning Research, 2002, 3: 463-482.
[4] Liu Y, Liao S Z. Frame kernel selection via explicit descrip-tion of integral operator space[J]. Scientia Sinica Informa-tionis, 2016, 46(2): 165-178.刘勇, 廖士中. 基于积分算子空间显式描述的框架核选择方法[J]. 中国科学: 信息科学, 2016, 46(2): 165-178.
[5] Zhang X, Liao Y, Liao S Z. A survey on online kernel selec-tion for online kernel learning[J]. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 2019, 9(2): e1295.
[6] Yang T B, Mahdavi M, Jin R, et al. Online kernel selection: algorithms and evaluations[C]//Proceedings of the 26th AAAI Conference on Artificial Intelligence, Toronto, Jul 22-26, 2012. Menlo Park: AAAI, 2012: 1197-1203.
[7] Chen B D, Liang J L, Zheng N N, et al. Kernel least mean square with adaptive kernel size[J]. Neurocomputing, 2016, 191: 95-106.
[8] Nguyen T D, Le T, Bui H, et al. Large-scale online kernel learning with random feature reparameterization[C]//Procee-dings of the 26th International Joint Conference on Artifi-cial Intelligence, Melbourne, Aug 19-25, 2017. San Mateo: Morgan Kaufmann, 2017: 2543-2549.
[9] Zhang X, Liao S Z. Online kernel selection via incremental sketched kernel alignment[C]//Proceedings of the 27th Inter-national Joint Conference on Artificial Intelligence, Stock-holm, Jul 13-19,?2018. San Mateo: Morgan Kaufmann, 2018: 3118-3124.
[10] Zhang X, Liao S Z. Online kernel selection with local regret[J]. Chinese Journal of Computers, 2019, 42(1): 61-72.张骁, 廖士中. 基于局部后悔的在线核选择[J]. 计算机学报, 2019, 42(1): 61-72.
[11] Shalev-Shwartz S. Online learning and online convex optimi-zation[J]. Foundations and Trends in Machine Learning, 2011, 4(2): 107-194.
[12] Borodin A, El-Yaniv R. Online computation and competitive analysis[M]. New York: Cambridge University Press, 1998.
[13] Andrew L L H, Barman S, Ligett K, et al. A tale of two me-trics: simultaneous bounds on competitiveness and regret [C]//Proceedings of the 26th Annual Conference on Learning Theory, Princeton University, Jun 12-14, 2013: 741-763.
[14] Buchbinder N, Naor J. The design of competitive online alg-orithms via a primal-dual approach[J]. Foundations and Trends in Theoretical Computer Science, 2009, 3(2/3): 93-263.
[15] Huang Z. Online primal dual: beyond linear programs[J]. SIGACT News, 2014, 45(4): 105-119.
[16] Borodin A, Linial N, Saks M E. An optimal on-line algorithm for metrical task system[J]. Journal of the ACM, 1992, 39(4): 745-763.
[17] Cesa-Bianchi N, Lugosi G. Prediction, learning, and games[M]. New York: Cambridge University Press, 2006.
[18] Buchbinder N, Chen S, Naor J S, et al. Unified algorithms for online learning and competitive analysis[C]//Proceed-ings of the 25th Annual Conference on Learning Theory, Edinburgh, Jun 25-27, 2012. |