Practical methods of optimization; (2nd ed.)
Practical methods of optimization; (2nd ed.)
Robust regression and outlier detection
Robust regression and outlier detection
The nature of statistical learning theory
The nature of statistical learning theory
Least Squares Support Vector Machine Classifiers
Neural Processing Letters
Optimal control by least squares support vector machines
Neural Networks
Support vector interval regression networks for interval regression analysis
Fuzzy Sets and Systems - Theme: Learning and modeling
Text classification: A least square support vector machine approach
Applied Soft Computing
CPBUM neural networks for modeling with outliers and noise
Applied Soft Computing
Hybrid approach of selecting hyperparameters of support vector machine for regression
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
The annealing robust backpropagation (ARBP) learning algorithm
IEEE Transactions on Neural Networks
Robust support vector regression networks for function approximation with outliers
IEEE Transactions on Neural Networks
Analysis of switching dynamics with competing support vector machines
IEEE Transactions on Neural Networks
A robust backpropagation learning algorithm for function approximation
IEEE Transactions on Neural Networks
Discovery of motifs to forecast outlier occurrence in time series
Pattern Recognition Letters
Matrix pattern based minimum within-class scatter support vector machines
Applied Soft Computing
Cross-document structural relationship identification using supervised machine learning
Applied Soft Computing
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In this study, a hybrid robust support vector machine for regression is proposed to deal with training data sets with outliers. The proposed approach consists of two stages of strategies. The first stage is for data preprocessing and a support vector machine for regression is used to filter out outliers in the training data set. Since the outliers in the training data set are removed, the concept of robust statistic is not needed for reducing the outliers' effects in the later stage. Then, the training data set except for outliers, called as the reduced training data set, is directly used in training the non-robust least squares support vector machines for regression (LS-SVMR) or the non-robust support vector regression networks (SVRNs) in the second stage. Consequently, the learning mechanism of the proposed approach is much easier than that of the robust support vector regression networks (RSVRNs) approach and of the weighted LS-SVMR approach. Based on the simulation results, the performance of the proposed approach with non-robust LS-SVMR is superior to the weighted LS-SVMR approach when the outliers exist. Moreover, the performance of the proposed approach with non-robust SVRNs is also superior to the RSVRNs approach.