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

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書目名稱Machine Learning and Knowledge Discovery in Databases影響因子(影響力)




書目名稱Machine Learning and Knowledge Discovery in Databases影響因子(影響力)學(xué)科排名




書目名稱Machine Learning and Knowledge Discovery in Databases網(wǎng)絡(luò)公開度




書目名稱Machine Learning and Knowledge Discovery in Databases網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Machine Learning and Knowledge Discovery in Databases被引頻次




書目名稱Machine Learning and Knowledge Discovery in Databases被引頻次學(xué)科排名




書目名稱Machine Learning and Knowledge Discovery in Databases年度引用




書目名稱Machine Learning and Knowledge Discovery in Databases年度引用學(xué)科排名




書目名稱Machine Learning and Knowledge Discovery in Databases讀者反饋




書目名稱Machine Learning and Knowledge Discovery in Databases讀者反饋學(xué)科排名

hypertension

節(jié)省

PALMY

Mammal

QUAIL

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

花爭吵

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

interrupt

易怒