Fuzzy data analysis by possibilistic linear models
Fuzzy Sets and Systems - Fuzzy Numbers
A practical approach to nonlinear fuzzy regression
SIAM Journal on Scientific and Statistical Computing
Fuzzy regression analysis using neural networks
Fuzzy Sets and Systems
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Fuzzy regression methods—a comparative assessment
Fuzzy Sets and Systems
Learning from Data: Concepts, Theory, and Methods
Learning from Data: Concepts, Theory, and Methods
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Mathematical Programming in Data Mining
Data Mining and Knowledge Discovery
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Support vector fuzzy regression machines
Fuzzy Sets and Systems - Theme: Learning and modeling
Support vector interval regression networks for interval regression analysis
Fuzzy Sets and Systems - Theme: Learning and modeling
A tutorial on support vector regression
Statistics and Computing
Linear and non-linear fuzzy regression: Evolutionary algorithm solutions
Fuzzy Sets and Systems
A revisited approach to linear fuzzy regression using trapezoidal fuzzy intervals
Information Sciences: an International Journal
Knowledge based Least Squares Twin support vector machines
Information Sciences: an International Journal
A class of fuzzy clusterwise regression models
Information Sciences: an International Journal
Simultaneous feature selection and classification using kernel-penalized support vector machines
Information Sciences: an International Journal
Robust fuzzy regression analysis
Information Sciences: an International Journal
Information Sciences: an International Journal
Information Sciences: an International Journal
Interval regression analysis by quadratic programming approach
IEEE Transactions on Fuzzy Systems
Interval regression analysis using quadratic loss support vector machine
IEEE Transactions on Fuzzy Systems
A study on reduced support vector machines
IEEE Transactions on Neural Networks
Reduced Support Vector Machines: A Statistical Theory
IEEE Transactions on Neural Networks
Multi-appliance recognition system with hybrid SVM/GMM classifier in ubiquitous smart home
Information Sciences: an International Journal
Information Sciences: an International Journal
Information Sciences: an International Journal
Recognizing architecture styles by hierarchical sparse coding of blocklets
Information Sciences: an International Journal
| Hi-index | 0.07 |
The support vector machine (SVM) has been shown to be an efficient approach for a variety of classification problems. It has also been widely used in pattern recognition, regression and distribution estimation for separable data. However, there are two problems with using the SVM model: (1) Large-scale: when dealing with large-scale data sets, the solution may be difficult to find when using SVM with nonlinear kernels; (2) Unbalance: the number of samples from one class is much larger than the number of samples from the other classes. It causes the excursion of separation margin. Under these circumstances, developing an efficient method is necessary. Recently, the use of the reduced support vector machine (RSVM) was proposed as an alternative to the standard SVM. It has been proven more efficient than the traditional SVM in processing large-scaled data. In this paper, we introduce the principle of RSVM to evaluate interval regression analysis. The main idea of the proposed method is to reduce the number of support vectors by randomly selecting a subset of samples.