Yahya Forghani

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The well-known support vector data description (SVDD) is based on precise description of precise data. When we know the features of training samples precisely and we are uncertain about their class labels, the fuzzy SVDD can be used to obtain the data description. But if the features of training samples are fuzzy numbers, the fuzzy SVDD cannot be utilized.(More)
A fuzzy min–max neural network with symmetric margin (FMNWSM) is proposed in this paper. Therefore, its probability of misclassification is lower than traditional fuzzy min–max neural networks if both training and test samples are from identical probability distribution. Meanwhile, data is classified with symmetric margin by the use of a non-linear program(More)
Because the SVM (support vector machine) classifies data with the widest symmetric margin to decrease the probability of the test error, modern fuzzy systems use SVM to tune the parameters of fuzzy if-then rules. But, solving the SVM model is time-consuming. To overcome this disadvantage, we propose a rapid method to solve the robust SVM model and use it to(More)
This paper comments on the published work dealing with robustness and regularization of 2009) by H. Xu, etc. They proposed a theorem to show that it is possible to relate robustness in the feature space and robustness in the sample space directly. In this paper, we propose a counter example that rejects their theorem. 1. Comment Firstly, it must be stated(More)
This paper comments on the published work dealing with robustness and regularization of support et al. They proposed a theorem to show that it is possible to relate robustness in the feature space and robustness in the sample space directly. In this paper, we propose a counter example that rejects their theorem. 1. Comment Firstly, it must be stated that Xu(More)
—The epsilon-SVR has two limitations. Firstly, the tube radius (epsilon) or noise rate along the-axis must be already specified. Secondly, this method is suitable for function estimation according to training data in which noise is independent of input (is constant). To resolving these limitations, in approaches like-SVIRN, the tube radius or the radius of(More)
—In this paper, we incorporate the concept of fuzzy set theory into the support vector regression (SVR). In our proposed method, target outputs of training samples are considered to be fuzzy numbers and then, membership function of actual output (objective hyperplane in high dimensional feature space) is obtained. Two main properties of our proposed method(More)
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