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An appropriate pre-processing algorithm in classification is important and crucial with respect to classifier type. In this paper, two pre-processing methods are suggested to be applied before classification in order to increase classification accuracy. The aim of this approach is finding a transformation matrix to discriminate between classes by(More)
Linear Discriminant Analysis (LDA) is a feature selection method in speech recognition. LDA finds transformations that maximizes the between-class scatter and minimizes within-class scatter. This transformation can be obtained in a class-dependent or class independent manner. In this paper, we propose a method to improve LDA and also we use it instead of(More)
Selection of a classifier is only one aspect of the problem of data classification. Equally important (if not, more so) is the pre-processing strategy to be employed. In this paper, a pre-processing step is proposed in order to increase accuracy of classification. The aim of this approach is finding a transformation matrix to discriminate between classes by(More)
Modern electrocardiography (ECG) uses a constructed reference potential for the majority of leads. This reference potential, named after its inventor as the Wilson central terminal, is assumed to have negligible value and to be stationary during the cardiac cycle. However, the problem of its variability during the cardiac cycle has been known almost since(More)
Over the past few years, Peer-to-Peer traffic has been consuming a lot of Internet traffic bandwidth and is still rising which brings great difficulties to network management. Traditional classification techniques such as port based and payload based have significant limitations. Hence, newer statistical approaches are adopted for P2P identification. P2P(More)
Reduced Ordered Binary Decision Diagrams (ROBDDs) are frequently used as the representation of choice to solve various CAD problems such as synthesis, digital-system verification and testing. The size of an ROBDD for a function can be increased exponentially by the number of independent variables of the function that is called "memory explosion problem".(More)