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Fuzzy Support Vector Machine (FSVM) has become a handy tool for many classification problems. FSVM provides flexibility of incorporating membership values to individual training samples. Performance of FSVM largely depends on how well these membership values are assigned to the training samples. Recently, a new approach for assigning membership values was proposed, where only possible outliers are...
In this paper, a new framework to build an adaptive classifier is introduced. At first, a clustering algorithm, density-based spatial clustering of applications with noise (DBSCAN) is applied to a set of sample data to form initial set of clusters. The clusters are represented as classes. Using support vector machine (SVM), a classifier model is generated. In real world application, data comes in...
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