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In this paper, we propose a new iterative learning control (ILC) scheme, which is devoted to dealing with unknown parameters that are both time varying and iteration varying. In particular, we consider iteration-varying parameters that are generated by a second-order internal model. By incorporating the internal model into the parametric learning law, the ILC scheme can handle more generic nonlinear...
This research aim to determine the explanation knowledge boundary for improvement of basic diagnosis. This paper compares different machine learning techniques including Maximum Entropy, Bayesian Networks, and Naive Bayes for solving the boundary determination problems of the discourse marker's connection problem, usage of several discourse markers within the boundary, and implicit discourse marker...
Anti-virus systems traditionally use signatures to detect malicious executables, but this method beyond the capability of many existing detection approaches. In this paper, we present a data mining approach to detect unknown malicious executables. The feature set is a key to applying data mining or machine learning to successfully detect malicious executables. We propose a method to extract features...
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