[1] CHAPELLE O, SINDHWANI V, KEERTHI S S. Optimization techniques for semi-supervised support vector machines[J]. Journal of Machine Learning Research, 2008, 9: 203-233. [2] BENNETT K P, DEMIRIZ A. Semi-supervised support vector machines[C]//Proceedings of the 1998 Conference on Advances in Neural Information Processing Systems II. Cambridge, MA: MIT Press, 1998: 368-374. [3] YANG L M, WANG L S. A class of smooth semi-supervised SVM by difference of convex functions programming and algorithm[J]. Knowledge-based Systems, 2013, 41: 1-7. [4] TEMPO R, CALAFIORE G, DABBENE F. Statistical learning theory[M]//Randomized Algorithms for Analysis and Control of Uncertain Systems: with Applications. London: Springer-Verlag, 2013: 123-134. [5] YANG L M, WANG L S, GAO Y P, et al. A convex relaxation framework for a class of semi-supervised learning methods and its application in pattern recognition[J]. Engineering Applications of Artificial Intelligence, 2014, 35: 335-344. [6] HUANG G-B, ZHU Q-Y, SIEW C-K. Extreme learning machine: theory and applications[J]. Neurocomputing, 2006, 70(1/2/3): 489-501. [7] HUANG G-B, DING X, ZHOU H. Optimization method based extreme learning machine for classification[J]. Neurocomputing, 2010, 74(1/2/3): 155-163. [8] MATIAS T, SOUZA F, ARAúJO R, et al. Learning of a single-hidden layer feedforward neural network using an optimized extreme learning machine[J]. Neurocomputing, 2014, 129: 428-436. [9] CHOROWSKI J, WANG J, ZURADA J M. Review and performance comparison of SVM-and ELM-based classifiers[J]. Neurocomputing, 2014, 128: 507-516. [10] LIU J F, CHEN Y Q, LIU M J, et al. SELM: Semi-supervised ELM with application in sparse calibrated location estimation[J]. Neurocomputing, 2011, 74(16): 2566-2572. [11] HUANG G, SONG S J, GUPTA J N D, et al. Semi-supervised and unsupervised extreme learning machines[J]. IEEE Transactions on Cybernetics, 2014, 44(12): 2405-2417. [12] HUANG G-B, CHEN L, SIEW C-K. Universal approximation using incremental constructive feedforward networks with random hidden nodes[J]. IEEE Transactions on Neural Networks, 2006, 17(4): 879-892. [13] 张海东,李贵荣,李若诚,等.近红外光谱结合极限学习机和GA-PLS算法检测普洱茶茶多酚含量[J].激光与光电子学进展,2013,50(4):180-186. (ZHANG H D, LI G R, LI R C, et al. Determination of tea polyphenols content in Puerh tea using near-infrared spectroscopy combined with extreme learning machine and GA-PLS algorithm[J]. Laser & Optoelectronics Progress, 2013, 50(4): 180-186.) [14] YANG L M, SUN Q. Recognition of the hardness of licorice seeds using a semi-supervised learning method and near-infrared spectral data[J]. Chemometrics & Intelligent Laboratory Systems, 2012, 114: 109-115. [15] LIU Q G, HE Q, SHI Z Z, et al. Extreme support vector machine classifier[C]//PAKDD 2008: Proceedings of the 12th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, LNCS 5012. Berlin: Springer-Verlag, 2008: 222-233. [16] FAWCETT T. An introduction to ROC analysis[J]. Pattern Recognition Letters, 2006, 27(8): 861-874. |