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

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Domain Adaptation with Regularized Optimal Transportnt increase in performance compared to the original transport solution. The proposed algorithm is computationally efficient and effective, as illustrated by its evaluation on a toy example and a challenging real life vision dataset, against which it achieves competitive results with respect to state-of-the-art methods.

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歌唱隊(duì)

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Concurrent Bandits and Cognitive Radio Networksh a collision avoidance mechanism. We analyze its regret with respect to the system-wide optimum and show that sub-linear regret can be obtained in this setting. Experiments show dramatic improvement compared to other algorithms for this setting.

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https://doi.org/10.1007/978-3-662-44848-9artificial intelligence; association rules; clustering; collaborative filtering; content ranking; data an

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