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Multi-graph-view Learning for Graph Classification
2014
2014 IEEE International Conference on Data Mining
In this paper, we propose to represent and classify complicated objects. In order to represent the objects, we propose a multi-graph-view model which uses graphs constructed from multiple graph-views to represent an object. In addition, a bag based multi-graph model is further used to relax labeling by only requiring one label for a bag of graphs, which represent one object. In order to learn classification models, we propose a multi-graph-view bag learning algorithm (MGVBL), which aims to
doi:10.1109/icdm.2014.97
dblp:conf/icdm/WuHPZCZ14
fatcat:2ax3yvc4mjg3nhw4uh6kwuj63i