Generalized Sparse Learning of Linear Models Over the Complete Subgraph Feature Set

Supervised learning over graphs is an intrinsically difficult problem: simultaneous learning of relevant features from the complete subgraph feature set, in which enumerating all subgraph features occurring in given graphs is practically intractable due to combinatorial explosion. We show that 1) ex...

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Bibliographische Detailangaben

Veröffentlicht in:	IEEE transactions on pattern analysis and machine intelligence. - 1979. - 39(2017), 3 vom: 17. März, Seite 617-624
1. Verfasser:	Takigawa, Ichigaku (VerfasserIn)
Weitere Verfasser:	Mamitsuka, Hiroshi
Format:	Online-Aufsatz
Sprache:	English
Veröffentlicht:	2017
Zugriff auf das übergeordnete Werk:	IEEE transactions on pattern analysis and machine intelligence
Schlagworte:	Journal Article Research Support, Non-U.S. Gov't