[1] PAN S J, YANG Q. A survey on transfer learning [J]. IEEE Transactions on Knowledge and Data Engineering, 2010,22(10):1345-1359. [2] 史荧中,王士同,蒋亦樟,等.迁移学习支持向量回归机[J].计算机应用,2013,33(11):3084-3089.(SHI Y Z, WANG S T, JIANG Y Z, et al. Transfer learning support vector regression [J]. Journal of Computer Applications, 2013,33(11):3084-3089.) [3] YANG P, TAN Q, DING Y. Bayesian task-level transfer learning for non-linear regression [C]//Proceedings of the 2008 International Conference on Computer Science and Software Engineering. Piscataway, NJ: IEEE, 2008:62-65. [4] XIE S, FAN W, PENG J, et al. Latent space domain transfer between high dimensional overlapping distributions [C]//Proceedings of the 18th International Conference on World Wide Web. New York: ACM, 2009:91-100. [5] DELGADO S, MORÁN F, MORA A, et al. A novel representation of genomic sequences for taxonomic clustering and visualization by means of self-organizing maps [J]. Bioinformatics, 2015,31(5):736-744. [6] 邵超,万春红.基于自组织映射的流形学习与可视化[J].计算机应用,2013,33(7):1917-1921.(SHAO C, WAN C H. Manifold learning and visualization based on self-organizing map [J]. Journal of Computer Applications, 2013,33(7):1917-1921.) [7] CHENG S S, FU H C, WANG H M. Model-based clustering by probabilistic self-organizing maps [J]. IEEE Transactions on Neural Networks, 2009,20(5):805-826. [8] LOPEZ-RUBIO E, PALOMO E J. Growing hierarchical probabilistic self-organizing graphs [J]. IEEE Transactions on Neural Networks, 2011, 22(7): 997-1008. [9] RESHEF D N, RESHEF Y A, FINUCANE H K, et al. Detecting novel associations in large data sets [J]. Science, 2011,334(6062):1518-1524. [10] GOSAVI A. Simulation-based optimization: an overview [M]//Simulation-Based Optimization. Berlin: Springer, 2015:29-35. [11] RAVIKUMAR P, WAINWRIGHT M J, LAFFERTY J D. High-dimensional ising model selection using l1-regularized logistic regression [J]. The Annals of Statistics, 2010,38(3):1287-1319. [12] ZHANG T, ZOU H. Sparse precision matrix estimation via lasso penalized D-trace loss [J]. Biometrika, 2014,101(1):103-120. [13] MUKHERJEE S, HILL S M. Network clustering: probing biological heterogeneity by sparse graphical models [J]. Biostatistics. 2011,27(7):994-1000. [14] SHIEH S L, LIAO I E. A new approach for data clustering and visualization using self-organizing maps [J]. Expert Systems with Applications, 2012,39(15):11924-11933. [15] DU K L, SWAMY M N S. Clustering Ⅱ: topics in clustering [M]//Neural Networks and Statistical Learning. Berlin: Springer, 2014:259-297. [16] FIANNACA A, DI FATTA G, RIZZO R, et al. Simulated annealing technique for fast learning of SOM networks [J]. Neural Computing and Applications, 2013,22(5):889-899. [17] LOPEZ-PAZ D, HENNIG P, SCHÖLKOPF B. The randomized dependence coefficient [EB/OL]. [2015-03-12]. http://xueshu.baidu.com/s?wd=paperuri%3A%282c086713a1bc514b5a3d07e5def52bd4%29&filter=sc_long_sign&tn=SE_xueshusource_2kduw22v&sc_vurl=http%3A%2F%2Farxiv.org%2Fpdf%2F1304.7717v1&ie=utf-8. [18] TAN B, ZHONG E, XIANG E W, et al. Multi-transfer: transfer learning with multiple views and multiple sources [J]. Statistical Analysis and Data Mining: the ASA Data Science Journal, 2014,7(4):282-293. [19] BICKEL S. ECML-PKDD discovery challenge 2006 overview [EB/OL]. [2015-03-09]. http://ceas2009.cc/discovery_challenge2006_overview.pdf. |