The nature of statistical learning theory
The nature of statistical learning theory
Support vector fuzzy regression machines
Fuzzy Sets and Systems - Theme: Learning and modeling
Thalassaemia classification by neural networks and genetic programming
Information Sciences: an International Journal
Extraction of fuzzy rules from support vector machines
Fuzzy Sets and Systems
Fuzzy functions with support vector machines
Information Sciences: an International Journal
On support vector regression machines with linguistic interpretation of the kernel matrix
Fuzzy Sets and Systems
Statistically monotonic and statistically bounded sequences of fuzzy numbers
Information Sciences: an International Journal
Hybrid approach of selecting hyperparameters of support vector machine for regression
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An overview of statistical learning theory
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
The Bounds on the Rate of Uniform Convergence of Learning Process on Uncertainty Space
ISNN '09 Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks
The Key Theorem of Learning Theory on Uncertainty Space
ISNN '09 Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks
A cognitive interactionist sentence parser with simple recurrent networks
Information Sciences: an International Journal
The key theorem of learning theory based on Sugeno measure and fuzzy random samples
LSMS/ICSEE'10 Proceedings of the 2010 international conference on Life system modeling and simulation and intelligent computing, and 2010 international conference on Intelligent computing for sustainable energy and environment: Part II
| Hi-index | 0.07 |
Statistical learning theory based on real-valued random samples has been regarded as a better theory on statistical learning with small sample. The key theorem of learning theory and bounds on the rate of convergence of learning processes are important theoretical foundations of statistical learning theory. In this paper, the theoretical foundations of the statistical learning theory based on fuzzy number samples are discussed. The concepts of fuzzy expected risk functional, fuzzy empirical risk functional and fuzzy empirical risk minimization principle are redefined. The key theorem of learning theory based on fuzzy number samples is proved. Furthermore, the bounds on the rate of convergence of learning processes based on fuzzy number samples are discussed.