Computer Science ›› 2017, Vol. 44 ›› Issue (8): 242-245.doi: 10.11896/j.issn.1002-137X.2017.08.041
Previous Articles Next Articles
ZHOU Chuang, FAN Bin, ZHU Lei and LU Xin-jiang
| [1] CHUANG C C,LEE Z J.Hybrid robust support vector ma-chines for regression with outliers[J].Applied Soft Computing,2011,11(1):64-72. [2] WEN W,HAO Z,YANG X.Robust least squares support vector machine based on recursive outlier elimination[J].Soft Computing,2010,14(11):1241-1251. [3] YOU C C,ZHANG G G.An effective search method based on large data [J].Computer Science,2013,40(6):183-186.(in Chinese) 尤川川,张桂刚.一种基于大数据的有效搜索方法[J].计算机科学,2013,40(6):183-186. [4] ZHANG G,LI H X,GAN M.Design a Wind Speed Prediction Model Using Probabilistic Fuzzy System[J].IEEE Transactions on Industrial Informatics,2012,8(4):819-827. [5] LI H X,LIU Z.A Probabilistic Neural-Fuzzy Learning System for Stochastic Modeling[J].IEEE Transactions on Fuzzy Systems,2008,16(4):898-908. [6] LIU Z,LI H X.A probabilistic fuzzy logic system for modeling and control[J].IEEE Transactions on Fuzzy Systems,2005,13(6):848-859. [7] BAI J,YANG L H,ZHANG X Y.An anti noise SVM parameter optimization method for speech recognition[J].Journal of Central South University (Natural Science Edition),2013,44(2):604-611.(in Chinese) 白静,杨利红,张雪英.一种面向语音识别的抗噪SVM参数优化方法[J].中南大学学报(自然科学版),2013,44(2):604-611. [8] HUANG G B,ZHU Q Y,SUEW C K.Extreme learning machine:Theory and applications[J].Neurocomputing,2006,70(1-3):489-501. [9] FOSSACECA J M,MAZZUCHI T A,SARKANI S.MARK-ELM:Application of a novel Multiple Kernel Learning framework for improving the robustness of Network Intrusion Detection[J].Expert Systems with Applications,2015,42(8):4062-4080. [10] FENG N Q,QIU Y H,WANG F.A new method for improving neural network generalization ability[J].Computer Science,2006,33(2):201-204.(in Chinese) 冯乃勤,邱玉辉,王芳.一种提高神经网络泛化能力的新方法[J].计算机科学,2006,33(2):201-204. [11] ZHOU L,LAI K K,YU L.Least squares support vector machines ensemble models for credit scoring[J].Expert Systems with Applications,2010,37(37):127-133. [12] ABE D S.Support Vector Machines for Pattern Classification[J].Advances in Pattern Recognition,2010,36(5):7535-7543. [13] ZHU Q Y,QIN A K,SUGANTHAN P N,et al.Evolutionary extreme learning machine[J].Pattern Recognition,2005,38(10):1759-1763. [14] LU X J,ZHOU C,HUANG M H,et al.Regularized online sequential extreme learning machine with adaptive regulation factor for time-varying nonlinear system[J].Neurocomputing,2016,174:617-626. [15] CHEN C,YAN C,LI Y.A robust weighted least squares support vector regression based on least trimmed squares[J].Neurocomputing,2015,168:941-946. [16] TIAN S F,HUANG H K.A Simplification Algorithm to Support Vector Machines for Regression[J].Journal of Software,2002,13(6):1169-1172. [17] SUYKENS J A K,BRABANTER J D,LUKAS L,et al.Weighted least squares support vector machines:robustness and sparse approximation[J].Neurocomputing,2002,48(1-4):85-105. [18] CHEN X,YANG J,LIANG J,et al.Recursive robust leastsquares support vector regression based on maximum correntropy criterion[J].Neurocomputing,2012,97(1):63-73. [19] HUANG G B,WANG D H,LAN Y.Extreme learning ma-chines:a survey[J].International Journal of Machine Learning &Cybernetics,2011,2(2):107-122. [20] BI H,LIANG H L,WANG Y.Resampling methods and machine learning[J].Journal of Computer,2009,32(5):862-877.(in Chinese) 毕华,梁洪力,王珏.重采样方法与机器学习[J].计算机学报,2009,32(5):862-877. [21] ZHANG H,MENG X W,LIU L,et al.Improved SAR imagetarget detection based on Parzen window algorithm[J].Computer Science,2015,42(S2):151-154.(in Chinese) 张颢,孟祥伟,刘磊,等.改进的基于Parzen窗算法的SAR图像目标检测[J].计算机科学,2015,42(S2):151-154. [22] LICHMAN M.UCI Machine Learning Repository[EB/OL].(2014-02-26) [2016-07-15].http://archive.ics.uci.edu/ml/datasets.html. [23] HORATA P,CHIEWCHANWATTANA S,S UNAT K.Robust extreme learning machine[J].Neurocomputing,2013,102(2):31-44. |
| No related articles found! |
|
||
Home