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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...
Support vector machine (SVM) is an effective method for resolving regression problem. However, tradition SVM is very sensitive to noises in the training sample. In order to overcome this problem, fuzzy support vector regression (FSVR) based on combining cluster center with affinity is proposed in this paper. The fuzzy membership is defined not only by the distance between a point and its cluster center,...
In this paper, we propose a hybrid cost controller to deal with complicated attributes and high controlling risk of construction cost based on fuzzy least squares support vector machines. In this controller, fuzzy membership and least squares support vector machines are combined together. Considering the specificity of project sample data, complex fuzzy membership function is employed to improve their...
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...
Logistic regression algorithm and SVM algorithm are two well-known classification algorithms but when the multi-collinearity between independent variables occurs in above two algorithms, their classifying performance will always be not good. An improved classification algorithm combining the Choquet integral with respect to the lambda-measure based on gamma-support is proposed by our previous work...
A new method was proposed for incorporating prior knowledge in the form of fuzzy knowledge sets into Support Vector Machine for regression problem. The prior knowledge of Fuzzy IF-THEN rules can be transformed into fuzzy information to generate fuzzy kernel, based on which FSVR (Fuzzy Support Vector Regression) is introduced. The merit of FSVR is that it can incorporate with prior knowledge represented...
This paper builds a SVR-based fuzzy system, which is combined by support vector machine and fuzzy system. Every rule corresponds to a small SVR, and subjection degree of every rule is obtained automatically from a neural network. It overcomes the disadvantage of manual-decided subjection degree in advance. From emulation test, we can see that it is a high accuracy and high effect system
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