Armin Daneshpazhouh

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The task of semi-supervised outlier detection is to find the instances that are exceptional from other data with the use of some labeled examples. In many applications such as fraud detection and intrusion detection, this issue becomes important. Most existing techniques are unsupervised and the semi-supervised approaches use both negative and positive(More)
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