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Fuzzy support vector machine (FSVM) have been very successful in pattern recognition problems with outliers or noises. FSVM enhances the SVM in reducing the effect of noises in data points. In this paper, we introduce FSVM to regression problems for function approximation with noises. We apply a fuzzy membership to each input point of SVR and reformulate SVR into fuzzy SVR (FSVR) such that different...
As a novel data mining method featured with excellent pattern recognition capability, Support vector machine (SVM) is utilized in this paper to detect erroneous remote measurement, and therefore, prevent mis-operation of automatic voltage control (AVC) systems. A SVM nonlinear regression algorithm is first used to predict remote measurement. The remote measurement deviate obviously from the predicted...
Support vector machine (SVM) which overcomes the drawbacks of neural networks has been widely used for pattern recognition in recent years. A new optimization method for the fault diagnosis model is proposed. To overcome the deficiencies of low accuracy and high false alarm rate in fault diagnosis system, an integrated fault diagnosis model based on support vector regression and principal components...
The performance and regression precision of weak learners (accuracies should be greater than 0.5) for pattern recognition and forecasting can be upgraded using AdaBoost algorithm. Support vector machine (SVM) is a state-of-the-art learning machines and have been widely used in pattern recognition area since 90's of 20th contrary, however the performance of SVM is not stable and easily influenced due...
As a powerful machine learning approach for pattern recognition problems, support vector machine is known to have good generalization ability. Based on the index system of enterprise's self-fulfillment capability, a new integrated evaluation model is established by using support vector regression method. The method has advantages of accuracy, convenience, reliability and rapidity. The method is illustrated...
The research on realizing the self-detecting damage function is one of the main research contents of smart structures, and an important issue related to the self-detecting damage function is the method of damage detection. It has been of an important theoretical meaning and a great practical value for applications of smart structures to research on this issue. Due to the structure damage detectionpsilas...
In this study we predict traffic speed on Istanbul roads using RTMS (remote traffic microwave sensor) speed measurements obtained from the Istanbul Municipality web site. We use two different pattern recognition methods, k-nearest neighbor (kNN) and support vector regression machine (SVM). In order to predict the speed at a short time (5 minutes to 60 minutes) ahead, we use speed measurements taken...
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