نتایج جستجو برای: genetic algorithms and support vector machines

تعداد نتایج: 16990544  

Journal: :Neural Computation 2000

Journal: :CoRR 2013
Duc-Hien Nguyen Manh-Thanh Le

Support vector machines (SVMs) and fuzzy rule systems are functionally equivalent under some conditions. Therefore, the learning algorithms developed in the field of support vector machines can be used to adapt the parameters of fuzzy systems. Extracting fuzzy models from support vector machines has the inherent advantage that the model does not need to determine the number of rules in advance....

Journal: :AMIA ... Annual Symposium proceedings. AMIA Symposium 2007
Alexander R. Statnikov Constantin F. Aliferis

Cancer diagnosis and clinical outcome prediction are among the most important emerging applications of gene expression microarray technology with several molecular signatures on their way toward clinical deployment. Use of the most accurate decision support algorithms available for microarray gene expression data is a critical ingredient in order to develop the best possible molecular signature...

2013
Changsheng Xiang Zhou Yu Xilong Qu

Parameters of support vector machines (SVM) which is optimized by standard genetic algorithm is easy to trap into the local minimum, in order to get the optimal parameters of support vector machine, this paper proposed a parameters optimization method for support vector machines based on improved genetic algorithm, the simulation experiment is carried out on 5 benchmark datasets. The simulation...

سید علی عظیمی محسن شفیعی نیک آبادی

Abstract—the purpose of this paper is to compare two artificial intelligence algorithms for forecasting supply chain demand. In first step data are prepared for entering into forecasting models. In next step, the modeling step, an artificial neural network and support vector machine is presented. The structure of artificial neural network is selected based on previous researchers' results. For ...

Accurate simulation runoff process can have a significant role in water resources management and related issues. The inherent complexity of  this process makes difficult the use of physical and numerical models. In recent years, application of intelligent models is increased a powerful tool in hydrological modeling. The aim of this study was the application of the Gamma test to select the optim...

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