日本語
 
Help Privacy Policy ポリシー/免責事項
  詳細検索ブラウズ

アイテム詳細


公開

会議論文

Semi-supervised Learning on Directed Graphs

MPS-Authors
/persons/resource/persons84330

Zhou,  D
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons84193

Schölkopf,  B
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons83975

Hofmann,  T
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

External Resource
There are no locators available
Fulltext (restricted access)
There are currently no full texts shared for your IP range.
フルテキスト (公開)
公開されているフルテキストはありません
付随資料 (公開)
There is no public supplementary material available
引用

Zhou, D., Schölkopf, B., & Hofmann, T. (2005). Semi-supervised Learning on Directed Graphs. Advances in Neural Information Processing Systems, 1633-1640.


引用: https://hdl.handle.net/11858/00-001M-0000-0013-D52B-B
要旨
Given a directed graph in which some of the nodes are labeled, we investigate the question of how to exploit the link structure of the graph to infer the labels of the remaining unlabeled nodes. To that extent we propose a regularization framework for functions defined over nodes of a directed graph that forces the classification function to change slowly on densely linked subgraphs. A powerful, yet computationally simple classification algorithm is derived within the proposed framework. The experimental evaluation on real-world Web classification problems demonstrates encouraging results that validate our approach.