This paper investigates the connection between additive fuzzy systems and kernel machines. We prove that, under quite general conditions, these two seemingly quite distinct models are essentially equivalent. As a result, algorithms based upon Support Vector (SV) learning are proposed to build fuzzy systems for classification and function approximation. The performance of the proposed algorithm ...

This paper discusses the application of support vector machine in the area of identification of nonlinear dynamical systems. The aim of this paper is to identify suitable model structure for nonlinear dynamic system. In this paper, Adaptive Neuro Fuzzy Inference Systems (ANFIS) and Support Vector Regression (SVR) models are applied for identification of highly nonlinear dynamic process. The res...

The 'kernel method' has attracted great attention with the development of support vector machine (SVM) and has been studied in a general way. In this paper, this 'method' is extended to the well-known fuzzy c-means (FCM) and possibilistic c-means (PCM) algorithms. It is realized by substitution of a kernel-induced distance metric for the original Euclidean distance, and the corresponding algori...

High dimensionality in data sets is one of the challenges faced classification, mining, and sentiment analysis. In set, many dimensionalities require effort to simplify. Many these have a major impact on complexity performance algorithms used for classification. Various were encountered, including how determine optimal combination pre-processing techniques, clean dataset, best classification al...

Tuberculosis is considered the main health threat in several parts of world. This ithe primary infectiousdisease causing this disease .When untreatedandundiagnosed, there ishigh mortalityrate patients.Thismethodincludes an automatic method to recognize tubersT his approach requires automated tuberculosis identification techniques reduce problematic illnesses. Extraction lung region first perfor...