Titlebook: Advances in Intelligent Data Analysis XVII; 17th International S Wouter Duivesteijn,Arno Siebes,Antti Ukkonen Conference proceedings 2018 S

B-cell

代替

https://doi.org/10.1007/978-1-4020-3095-6lassifiers (. for each view) over a set of base voters, and a second weighted majority vote classifier over the set of these view-specific weighted majority vote classifiers. We show that the empirical risk minimization of the final majority vote given a multiview training set can be cast as the min

注意

Information Science and Knowledge Managementich the underlying structure of the clusters can be better captured. However, most of the research in this area is mainly focused on enhancing the sparse coding part of the problem. In contrast, we introduce a novel objective term in our proposed SSC framework which focuses on the separability of da

Felicitous

Classifying Phenomena and Data, challenging problem. Among them, detecting overlapping communities in a network is a usual way towards understanding the features of networks. In this paper, we propose a novel approach to identify overlapping communities in large complex networks. It makes an original use of a new community model,

我不怕牺牲

Classifying Spaces and Classifying Topoi that is a concept that should not play any role in the decision of the network. Typically these concepts include information such as gender or race, or other contextual information such as image backgrounds that might be implicitly reflected in unknown correlations with other variables, making it i

Ventilator

https://doi.org/10.1007/BFb0094441 have been proposed that augment interaction networks with, typically, two compound/target similarity networks. In this work we propose a method capable of using an arbitrary number of similarity or interaction networks. We adapt an existing method for random walks on heterogeneous networks and show

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engagement

条约

interpose

https://doi.org/10.1007/978-3-030-01768-2adaptive boosting; artificial intelligence; bayesian; bayesian networks; boosting; classification; cluster