Pattern recognition is a collection of computer techniques to classify various observations into different clusters of similar attributes in either supervised or unsupervised manner. Application of fuzzy logic to unsupervised classification or clustering methods has resulted in many wildly used techniques such as fuzzy c-means (FCM) method. However, when the observations are too noisy, the perf...

Recently, Extreme Learning Machine(ELM) has been a promising tool in solving a large range of regression applications. However, to our best knowledge, there are very few researches applying ELM to estimate mixture regression model. To improve the estimation performance, this paper extends the classical ELM to the scenario of mixture regression. First, based on the idea of fuzzy clustering, a se...

The success of machine learning methods for inducing models from data crucially depends on the proper incorporation of background knowledge about the model to be learned. The idea of constraint-regularized learning is to employ fuzzy set-based modeling techniques in order to express such knowledge in a flexible way, and to formalize it in terms of fuzzy constraints. Thus, background knowledge c...

In this paper, we present a novel algorithm for adap-tive fuzzy segmentation of MRI data and estimation of intensity inhomogeneities using fuzzy logic. MRI intensity inhomogeneities can be attributed to imperfections in the RF coils or some problems associated with the acquisition sequences. The result is a slowly-varying shading artifact over the image that can produce errors with conventional...

—The research presents -hierarchical fuzzy twin support vector regression (-HFTSVR) based on -fuzzy twin support vector regression (-FTSVR) and -twin support vector regression (-TSVR). -FTSVR is achieved by incorporating trapezoidal fuzzy numbers to -TSVR which takes care of uncertainty existing in forecasting problems. -FTSVR determines a pair of -insensitive proximal functions by so...

The solution of the prediction problem is presented for the finite possibilistic modelling [3,4,6,7]. A recurrent variant of finite possibilistic models is considered. In this variant, we define the regularization condition for constructing a quasi-optimal estimator of fuzzy transition operator (FTO). We construct the discrete recurrent extremal fuzzy process with possibilistic uncertainty, the...

Recently, tuning the weights of the rules in Fuzzy Rule-Base Classification Systems is researched in order to improve the accuracy of classification. In this paper, a margin-based optimization model, inspired by Support Vector Machine classifiers, is proposed to compute these fuzzy rule weights. This approach not only considers both accuracy and generalization criteria in a single objective fun...

This paper presents a new algorithm for fuzzy c-regression model clustering. The proposed methodology is based on adding a second regularization term in the objective function of a Fuzzy C-Regression Model (FCRM) clustering algorithm in order to take into account noisy data. In addition, a new error measure is used in the objective function of the FCRM algorithm, replacing the one used in this ...

In this paper, we present a novel algorithm for adaptive fuzzy segmentation of MRI data and estimation of intensity inhomogeneities using fuzzy logic. MRI intensity inhomogeneities can be attributed to imperfections in the RF coils or some problems associated with the acquisition sequences. The result is a slowly-varying shading artifact over the image that can produce errors with conventional ...