Self-adaptive robust nonlinear regression for unknown noise via mixture of Gaussians
暂无分享,去创建一个
Qinghua Hu | Haibo Wang | Yun Wang | Q. Hu | Yun Wang | Haibo Wang
[1] Marina Thottan,et al. Anomaly detection in IP networks , 2003, IEEE Trans. Signal Process..
[2] Ping-Feng Pai,et al. Solar power output forecasting using evolutionary seasonal decomposition least-square support vector regression , 2016 .
[3] R. Brereton,et al. Crucial problems in regression modelling and their solutions. , 2002, The Analyst.
[4] Xindong Wu,et al. The Top Ten Algorithms in Data Mining , 2009 .
[5] Qinghua Hu,et al. Short-Term Wind Speed or Power Forecasting With Heteroscedastic Support Vector Regression , 2016, IEEE Transactions on Sustainable Energy.
[6] Hwa Jen Yap,et al. A Constrained Optimization based Extreme Learning Machine for noisy data regression , 2016, Neurocomputing.
[7] Jie Hu,et al. A novel support vector regression for data set with outliers , 2015, Appl. Soft Comput..
[8] G CanchoVicente,et al. Bayesian nonlinear regression models with scale mixtures of skew-normal distributions , 2011 .
[9] Johan A. K. Suykens,et al. Robustness of Kernel Based Regression: A Comparison of Iterative Weighting Schemes , 2009, ICANN.
[10] Yuichiro Anzai,et al. Pattern Recognition and Machine Learning , 1992, Springer US.
[11] Sergios Theodoridis,et al. Robust Linear Regression Analysis— A Greedy Approach , 2014, IEEE Transactions on Signal Processing.
[12] V. Maz'ya,et al. On approximate approximations using Gaussian kernels , 1996 .
[13] Xiaowei Yang,et al. A heuristic weight-setting strategy and iteratively updating algorithm for weighted least-squares support vector regression , 2008, Neurocomputing.
[14] Zhongguang Fu,et al. Outliers detection method of multiple measuring points of parameters in power plant units , 2015 .
[15] Girijesh Prasad,et al. Gaussian Mixture Model-based noise reduction in resting state fMRI data , 2013, Journal of Neuroscience Methods.
[16] Aldo M. Garay,et al. Heteroscedastic nonlinear regression models based on scale mixtures of skew-normal distributions. , 2011, Statistics & probability letters.
[17] J. Militký,et al. Detection of single influential points in OLS regression model building , 2001 .
[18] P. J. García Nieto,et al. A hybrid PSO optimized SVM-based model for predicting a successful growth cycle of the Spirulina platensis from raceway experiments data , 2016, J. Comput. Appl. Math..
[19] V. H. Lachos,et al. Robust mixture regression modeling based on scale mixtures of skew-normal distributions , 2016 .
[20] Giuliano Galimberti,et al. A multivariate linear regression analysis using finite mixtures of t distributions , 2014, Comput. Stat. Data Anal..
[21] Michael Georgiopoulos,et al. A fast outlier detection strategy for distributed high-dimensional data sets with mixed attributes , 2010, Data Mining and Knowledge Discovery.
[22] Yan-Lin He,et al. A novel robust regression model based on functional link least square (FLLS) and its application to modeling complex chemical processes , 2016 .
[23] Azuraliza Abu Bakar,et al. Outlier detection based on rough sets theory , 2009, Intell. Data Anal..
[24] Henry Leung,et al. A variational Bayesian approach to robust sensor fusion based on Student-t distribution , 2013, Inf. Sci..
[25] X. Zhuang,et al. Gaussian mixture density modeling of non-Gaussian source for autoregressive process , 1995, IEEE Trans. Signal Process..
[26] Ping Zhong,et al. Robust non-convex least squares loss function for regression with outliers , 2014, Knowl. Based Syst..
[27] Jian Yang,et al. Recursive robust least squares support vector regression based on maximum correntropy criterion , 2012, Neurocomputing.
[28] Jugal K. Kalita,et al. A multi-step outlier-based anomaly detection approach to network-wide traffic , 2016, Inf. Sci..
[29] Yifei Wang,et al. A normal least squares support vector machine (NLS-SVM) and its learning algorithm , 2009, Neurocomputing.
[30] Heteroscedastic nonlinear regression models based on scale mixtures of skew-normal distributions. , 2011 .
[31] Milan Meloun,et al. Statistical Data Analysis: A Practical Guide , 2011 .
[32] Christian Pierdzioch,et al. A real-time quantile-regression approach to forecasting gold returns under asymmetric loss , 2015 .
[33] Junsheng Zhao,et al. Adaptive natural gradient learning algorithms for Mackey-Glass chaotic time prediction , 2015, Neurocomputing.
[34] Dipak K. Dey,et al. Bayesian nonlinear regression models with scale mixtures of skew-normal distributions: Estimation and case influence diagnostics , 2011, Comput. Stat. Data Anal..
[35] Johan A. K. Suykens,et al. Weighted least squares support vector machines: robustness and sparse approximation , 2002, Neurocomputing.
[36] Deyu Meng,et al. Robust Matrix Factorization with Unknown Noise , 2013, 2013 IEEE International Conference on Computer Vision.
[37] Lan Bai,et al. Weighted Lagrange ε-twin support vector regression , 2016, Neurocomputing.
[38] Qinghua Hu,et al. Noise model based v-support vector regression with its application to short-term wind speed forecasting , 2014, Neural Networks.
[39] Chuanfa Chen,et al. A robust weighted least squares support vector regression based on least trimmed squares , 2015, Neurocomputing.
[40] Raymond T. Ng,et al. Distance-based outliers: algorithms and applications , 2000, The VLDB Journal.
[41] Jian Yang,et al. Robust nuclear norm regularized regression for face recognition with occlusion , 2015, Pattern Recognit..
[42] Xiaowei Yang,et al. A robust least squares support vector machine for regression and classification with noise , 2014, Neurocomputing.
[43] Geoffrey J. McLachlan,et al. Mixture models : inference and applications to clustering , 1989 .
[44] Luis Alonso,et al. Multioutput Support Vector Regression for Remote Sensing Biophysical Parameter Estimation , 2011, IEEE Geoscience and Remote Sensing Letters.
[45] Lin Li,et al. Multi-output least-squares support vector regression machines , 2013, Pattern Recognit. Lett..