Support vector machine (SVM) has been very successful in pattern recognition and function estimation problems. In this paper, we introduce the use of SVM for multivariate fuzzy linear and nonlinear regression models. Using the basic idea underlying SVM for multivariate fuzzy regressions gives comput
Relevance regression learning with support vector machines
β Scribed by Bruno Apolloni; Dario Malchiodi; Lorenzo Valerio
- Publisher
- Elsevier Science
- Year
- 2010
- Tongue
- English
- Weight
- 514 KB
- Volume
- 73
- Category
- Article
- ISSN
- 0362-546X
No coin nor oath required. For personal study only.
π SIMILAR VOLUMES
## Abstract A discrete state space is often the subject of conventional reinforcement learning; continuous states must be discretized in order to be handled with conventional learning methods. However, simple discretization increases the state dimensionality and results in an exponential increase i
## Abstract This article employs Support Vector Machine (SVM) and Relevance Vector Machine (RVM) for prediction of Evaporation Losses (__E__) in reservoirs. SVM that is firmly based on the theory of statistical learning theory, uses regression technique by introducing Ξ΅βinsensitive loss function ha