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A novel dual membership based fuzzy support vector machine (DM-FSVM) is presented while traditional fuzzy support vector machine (FSVM) is anal sized. There is only one membership in the samples of training sets of traditional SVM model, but in DM-FSVM, there are two memberships. The theoretically and simulate experiments show that this new method not only can keep the advantages of traditional FSVM,...
Support vector machine is effective method for resolving non-liner classification and regression problem, but it is sensitive to the noises and outliers in the training samples. In order to overcome this problem, fuzzy support vector machine (FSVM) is introduced. How to choose a proper fuzzy membership is very important for the practical problem in FSVM. Generally, fuzzy membership is built according...
This paper introduces a method of learning kernel by fuzzy equivalence relation (FER) based on prior knowledge. Firstly, prior knowledge is represented through fuzzy membership functions and fuzzy inference rules. Consequently features of prior knowledge are obtained by proper inference methods. Secondly, the learning rules of FER-kernel are obtained in terms of FER semantic interpretation and fuzzy...
The kernel-based clustering has attracted great attention with the development of support vector machine. One can perform a clustering approach in an image space after mapping the data in an original space to the image space, but it is difficult to capture the optimal parameters for finding real clusters. In this paper, we present a kernel-based clustering approach in light of a relational fuzzy clustering...
Clustering with constraints is an active area in machine learning and data mining. In this paper, a semi-supervised kernel-based fuzzy C-means algorithm called PCKFCM is proposed which incorporates both semi-supervised learning technique and the kernel method into traditional fuzzy clustering algorithm. The clustering is achieved by minimizing a carefully designed objective function. A kernel-based...
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