Fuzzy data analysis by possibilistic linear models
Fuzzy Sets and Systems - Fuzzy Numbers
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Advances in Large Margin Classifiers
Advances in Large Margin Classifiers
Support Vector Machines for Texture Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Intelligent Systems
Support vector fuzzy regression machines
Fuzzy Sets and Systems - Theme: Learning and modeling
ϵ-insensitive fuzzy c-regression models: introduction to ϵ-insensitive fuzzy modeling
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An overview of statistical learning theory
IEEE Transactions on Neural Networks
Functional equivalence between radial basis function networks and fuzzy inference systems
IEEE Transactions on Neural Networks
Reduced-set vector-based interval type-2 fuzzy neural network
WSEAS Transactions on Computers
Computers & Mathematics with Applications
Reduced-set vector learning based on hybrid kernels for interval type 2 fuzzy modeling
ICS'08 Proceedings of the 12th WSEAS international conference on Systems
Balanced bootstrap resampling method for neural model selection
Computers & Mathematics with Applications
Soft Computing and Learning Techniques in the Modeling of Humanistic Systems
International Journal of Artificial Life Research
| Hi-index | 0.09 |
Neural-fuzzy systems have been proved to be very useful and have been applied to modeling many humanistic problems. But these systems also have problems such as those of generalization, dimensionality, and convergence. Support vector machines, which are based on statistical learning theory and kernel transformation, are powerful modeling tools. However, they do not have the ability to represent and to aggregate vague and ill-defined information. In this paper, these two systems are combined. The resulting support vector fuzzy adaptive network (SVFAN) overcomes some of the difficulties of the neural-fuzzy system. To illustrate the proposed approach, a simple nonlinear function is estimated by first generating the training and testing data needed. The results show that the proposed network is a useful modeling tool.