Detection of abrupt changes: theory and application
Detection of abrupt changes: theory and application
Combining labeled and unlabeled data with co-training
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A martingale framework for concept change detection in time-varying data streams
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Adaptive support vector machine for time-varying data streams using martingale
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Performance characterization of video-shot-change detection methods
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The martingale framework for detecting changes in data stream, currently only applicable to labeled data, is extended here to unlabeled data using clustering concept. The one-pass incremental changedetection algorithm (i) does not require a sliding window on the data stream, (ii) does not require monitoring the performance of the clustering algorithm as data points are streaming, and (iii) works well for high-dimensional data streams. To enhance the performance of the martingale change detection method, the multiple martingale test method using multiple views is proposed. Experimental results show (i) the feasibility of the martingale method for detecting changes in unlabeled data streams, and (ii) the multiple-martingale test method compares favorably with alternative methods using the recall and precision measures for the video-shot change detection problem.