A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Environmental Modelling & Software
Generalization performance of support vector machines and neural networks in runoff modeling
Expert Systems with Applications: An International Journal
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Fuzzy Weighted Support Vector Regression With a Fuzzy Partition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
On the stability issues of linear Takagi-Sugeno fuzzy models
IEEE Transactions on Fuzzy Systems
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This research effort aims in the application of soft computing techniques toward water resources management. More specifically, the target is the development of reliable soft computing models capable of estimating the water supply for the case of ''Germasogeia'' mountainous watersheds in Cyprus. Initially, @e-Regression Support Vector Machines (@e-RSVM) and fuzzy weighted @e-RSVMR models have been developed that accept five input parameters. At the same time, reliable artificial neural networks have been developed to perform the same job. The 5-fold cross validation approach has been employed in order to eliminate bad local behaviors and to produce a more representative training data set. Thus, the fuzzy weighted Support Vector Regression (SVR) combined with the fuzzy partition has been employed in an effort to enhance the quality of the results. Several rational and reliable models have been produced that can enhance the efficiency of water policy designers.