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Titlebook: Smile Pricing Explained; Peter Austing Book 2014 Palgrave Macmillan, a division of Macmillan Publishers Limited 2014 Volatility smile.Impl

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发表于 2025-3-28 17:10:33 | 显示全部楼层
Peter Austing and propose FastDEC, a clustering framework by fast dominance estimation. The nearest density higher (NDH) relation and dominance-component (DC), more specifically their integration with the .-NN graph, are formally defined and theoretically analyzed. FastDEC includes two extensions to satisfy diff
发表于 2025-3-28 20:30:07 | 显示全部楼层
working by querying it? Specifically, we assume the feature space ℝ. is known and the kernel machine has . support vectors such that . > . (or . > > .), and in addition, the classifier C is laconic in the sense that for a feature vector, it only provides a predicted label (±1) without divulging oth
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发表于 2025-3-29 06:05:03 | 显示全部楼层
Peter Austinge will be found within a voice clip, even if its location varies between speakers. First, each parent voice clip is decomposed into overlapping sub-clips. Each sub-clip is then independently classified. Further, the Emotion Strength of the sub-classifications is scored to form a sub-classification a
发表于 2025-3-29 09:08:30 | 显示全部楼层
Peter Austingtasks, human evaluations are essential for measuring the true performance of tag classifiers, which traditional evaluation methods will consistently underestimate. We focus on the domain of tagging music clips, and demonstrate our results using data collected with a human computation game called Tag
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发表于 2025-3-29 19:35:40 | 显示全部楼层
Peter Austingod handles network graph data, temporal sequence data as well as user mobility data stemming from original CDRs data. We show the relevance of our methodology on real-world CDRs data from Ivory Coast for various case studies, like network planning strategy and yield management pricing strategy.
发表于 2025-3-29 21:40:03 | 显示全部楼层
发表于 2025-3-30 00:50:48 | 显示全部楼层
发表于 2025-3-30 06:45:40 | 显示全部楼层
Peter Austingfunction is optimized to map the ranking scores to actual survey satisfaction scores. This approach produces more accurate predictions than standard regression and classification approaches that directly fit the survey scores with call data, and can be easily generalized to other customer satisfacti
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