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We present an ensemble method for concept drift that dynamically creates and removes weighted experts in response to changes in performance. The method, dynamic weighted majority (DWM), uses four mechanisms to cope with concept drift: It trains online learners of the ensemble, it weights those learners based on their performance, it removes them, also based(More)
Algorithms for tracking concept drift are important for many applications. We present a general method based on the Weighted Majority algorithm for using any on-line learner for concept drift. Dynamic Weighted Majority (dwm) maintains an ensemble of base learners, predicts using a weighted-majority vote of these " experts " , and dynamically creates and(More)
We describe the use of machine learning and data mining to detect and classify malicious exe-cutables as they appear in the wild. We gathered 1, 971 benign and 1, 651 malicious executables and encoded each as a training example using n-grams of byte codes as features. Such processing resulted in more than 255 million distinct n-grams. After selecting the(More)
In this paper, we examine the use of machine learning to improve a rooftop detection process, one step in a vision system that recognizes buildings in overhead imagery. We review the problem of analyzing aerial images and describe an existing system that detects buildings in such images. We briefly review four algorithms that we selected to improve rooftop(More)
Malicious insiders do great harm and avoid detection by using their legitimate privileges to steal information that is often outside the scope of their duties. Based on information from public cases, consultation with domain experts, and analysis of a massive collection of information-use events and contextual information, we developed an approach for(More)