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Pattern recognition techniques have been widely used in security-sensitive applications to distinguish malicious samples from legitimate ones. However, there usually exist some intelligent attackers who intend to have malicious samples to be mis-classified as legitimate at test time, i.e. evasion attack. Current researches show that traditional Support Vector Machines (SVMs) are vulnerable to evasion...
Support vector machine (SVM) is an algorithm based on structure risk minimizing principle and has high generalization ability, but sometimes we prefer to incremental learning algorithms to handle very vast data for training SVM is very costly in time and memory consumption or because the data available are obtained at different intervals. SVM works well for incremental learning model with impressive...
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