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A key assumption underlying traditional supervised learning algorithms is that labeled examples used to train a classifier are drawn i.i.d. from the same distribution as test samples. This assumption is violated when classifying a test sample whose statistics differ from the training samples because the test signal is the output of a noisy linear time-invariant system, e.g., from channel propagation...
This paper presents a new technique for harmonic detection, estimation and elimination in power system. The approach expresses the input signal in the form of Fourier Transformation and adjusts the Fourier coefficients using Linear Adaptive Filters (Adaline). Two of the existing approaches using conventional Fast Fourier transformation (FFT) and FFT with modified W-H learning have been discussed with...
We present a robust probabilistic method to classify targets based on their tracks. As is customary in supervised learning problems, it is assumed that example tracks from various classes are available to train a classifier. We present an optimal but computationally intensive sequential solution, and show that a computationally feasible naive Bayes approximation works better than ignoring sequential...
We present a tool called BugFix that can assist developers in fixing program bugs. Our tool automatically analyzes the debugging situation at a statement and reports a prioritized list of relevant bug-fix suggestions that are likely to guide the developer to an appropriate fix at that statement. BugFix incorporates ideas from machine learning to automatically learn from new debugging situations and...
Service desks are used by customers to report IT issues in enterprise systems. Most of these service requests are resolved by level-1 persons (service desk attendants) by providing information/quick-fix solutions to customers. For each service request, level- 1 personnel identify important keywords and see if the incoming request is similar to any historic incident. Otherwise, an incident ticket is...
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