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Concept drift is a phenomenon typically experienced when data distributions change continuously over a period of time. In this paper we propose a cost-sensitive boosting approach for learning under concept drift. The proposed methodology estimates relevance costs of 'old' data samples w.r.t. to 'newer' samples and integrates it into the boosting process. We(More)
i ABSTRACT Real-world environments are characterized by non-stationary and continuously evolving data. Learning a classification model on this data would require a framework that is able to adapt itself to newer circumstances. Under such circumstances, transfer learning has come to be a dependable methodology for improving classification performance with(More)
Online advertising is dominated by traditional techniques such as pop ups, banners, emails etc. Users are more likely to engage with content they find relevant and interesting, and ads generally disrupt their browsing experience. Hence an effective advertisement is one that simultaneously satisfies the marketing goals of the advertiser and also seamlessly(More)
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