Titlebook: Machine Learning and Knowledge Discovery in Databases; European Conference, Toon Calders,Floriana Esposito,Rosa Meo Conference proceedings

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书目名称Machine Learning and Knowledge Discovery in Databases影响因子(影响力)




书目名称Machine Learning and Knowledge Discovery in Databases影响因子(影响力)学科排名




书目名称Machine Learning and Knowledge Discovery in Databases网络公开度




书目名称Machine Learning and Knowledge Discovery in Databases网络公开度学科排名




书目名称Machine Learning and Knowledge Discovery in Databases被引频次




书目名称Machine Learning and Knowledge Discovery in Databases被引频次学科排名




书目名称Machine Learning and Knowledge Discovery in Databases年度引用




书目名称Machine Learning and Knowledge Discovery in Databases年度引用学科排名




书目名称Machine Learning and Knowledge Discovery in Databases读者反馈




书目名称Machine Learning and Knowledge Discovery in Databases读者反馈学科排名

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Kernel Principal Geodesic Analysistations of data via the mapping to kernel feature space. Conventionally, kPCA relies on Euclidean statistics in kernel feature space. However, Euclidean analysis can make kPCA inefficient or incorrect for many popular kernels that map input points to a . in kernel feature space. To address this prob

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Attributed Graph Kernels Using the Jensen-Tsallis ,-Differenceseen probability distributions over the graphs. To this end, we first assign a probability to each vertex of the graph through a continuous-time quantum walk (CTQW). We then adopt the tree-index approach [1] to strengthen the original vertex labels, and we show how the CTQW can induce a probability d

Kaleidoscope

Sub-sampling for Multi-armed Bandits novel algorithm that is based on sub-sampling. Despite its simplicity, we show that the algorithm demonstrates excellent empirical performances against state-of-the-art algorithms, including Thompson sampling and KL-UCB. The algorithm is very flexible, it does need to know a set of reward distribut

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