Robust regression and outlier detection
Robust regression and outlier detection
Dictionary/outline of basic statistics
Dictionary/outline of basic statistics
Fuzzy regression analysis using neural networks
Fuzzy Sets and Systems
Fuzzy linear regression with fuzzy intervals
Fuzzy Sets and Systems
The nature of statistical learning theory
The nature of statistical learning theory
Robust interval regression analysis using neural networks
Fuzzy Sets and Systems
Fuzzy regression using asymmetric fuzzy coefficients and fuzzified neural networks
Fuzzy Sets and Systems
Fuzzy regression wiht radial basis function network
Fuzzy Sets and Systems
Neural Networks for Optimization and Signal Processing
Neural Networks for Optimization and Signal Processing
Comparing support vector machines with Gaussian kernels to radialbasis function classifiers
IEEE Transactions on Signal Processing
The annealing robust backpropagation (ARBP) learning algorithm
IEEE Transactions on Neural Networks
Extended support vector interval regression networks for interval input-output data
Information Sciences: an International Journal
Annealing robust fuzzy basis function for modelling with noise and outliers
International Journal of Computer Applications in Technology
Asymmetrical interval regression using extended ε -SVM with robust algorithm
Fuzzy Sets and Systems
Interval regression analysis using support vector networks
Fuzzy Sets and Systems
A Robust Support Vector Regression Based on Fuzzy Clustering
IEA/AIE '09 Proceedings of the 22nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: Next-Generation Applied Intelligence
Interval Regression Analysis with Soft-Margin Reduced Support Vector Machine
IEA/AIE '09 Proceedings of the 22nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: Next-Generation Applied Intelligence
Support vector machine for classification based on fuzzy training data
Expert Systems with Applications: An International Journal
On support vector regression machines with linguistic interpretation of the kernel matrix
Fuzzy Sets and Systems
Support vector interval regression machine for crisp input and output data
Fuzzy Sets and Systems
A fuzzy support vector regression model for business cycle predictions
Expert Systems with Applications: An International Journal
A ν-twin support vector machine (ν-TSVM) classifier and its geometric algorithms
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
Hybrid robust support vector machines for regression with outliers
Applied Soft Computing
Conservative and aggressive rough SVR modeling
Theoretical Computer Science
Interval regression by tolerance analysis approach
Fuzzy Sets and Systems
A reduced support vector machine approach for interval regression analysis
Information Sciences: an International Journal
Information Sciences: an International Journal
Semidefinite Programming-Based Method for Implementing Linear Fitting to Interval-Valued Data
International Journal of Fuzzy System Applications
Extending twin support vector machine classifier for multi-category classification problems
Intelligent Data Analysis
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In this paper, the support vector interval regression networks (SVIRNs) are proposed for the interval regression analysis. The SVIRNs consist of two radial basis function networks. One network identifies the upper side of data interval, and the other network identifies the lower side of data intervals. Because the support vector regression (SVR) approach is equivalent to solving a linear constrained quadratic programming problem, the number of hidden nodes and the initial values of adjustable parameters can be easily obtained. Since the selection of a parameter ε in the SVR approach may seriously affect the modeling performance, a two-step approach is proposed to properly select the ε value. After the SVR approach with the selected ε, an initial structure of SVIRNs can be obtained. Besides, outliers will not significantly affect the upper and lower bound interval obtained through the proposed two-step approach. Consequently, a traditional back-propagation (BP) learning algorithm can be used to adjust the initial structure networks of SVIRNs under training data sets without or with outliers. Due to the better initial structure of SVIRNs are obtained by the SVR approach, the convergence rate of SVIRNs is faster than the conventional networks with BP learning algorithms or with robust BP learning algorithms for interval regression analysis. Four examples are provided to show the validity and applicability of the proposed SVIRNs.