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  Learning from Labeled and Unlabeled Data on a Directed Graph

Zhou, D., Huang, J., & Schölkopf, B. (2005). Learning from Labeled and Unlabeled Data on a Directed Graph. In S. Jozef Stefan Institute, Slovenia Program Chairs: Luc D, L. de Raedt, & S. Wrobel (Eds.), ICML '05: 22nd international conference on Machine learning (pp. 1036-1043). New York, NY, USA: ACM Press.

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 Creators:
Zhou, D1, 2, Author           
Huang, J, Author           
Schölkopf, B1, 2, Author           
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: We propose a general framework for learning from labeled and
unlabeled data on a directed graph in which the structure of the
graph including the directionality of the edges is considered. The
time complexity of the algorithm derived from this framework is
nearly linear due to recently developed numerical techniques. In
the absence of labeled instances, this framework can be utilized
as a spectral clustering method for directed graphs, which
generalizes the spectral clustering approach for undirected
graphs. We have applied our framework to real-world web
classification problems and obtained encouraging results.

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 Dates: 2005-08
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: 3463
DOI: 10.1145/1102351.1102482
 Degree: -

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Title: 22nd International Conference on Machine Learning (ICML 2005)
Place of Event: Bonn, Germany
Start-/End Date: 2005-08-07 - 2005-08-11

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Title: ICML '05: 22nd international conference on Machine learning
Source Genre: Proceedings
 Creator(s):
Jozef Stefan Institute, Slovenia Program Chairs: Luc D, S, Editor
de Raedt, L, Editor
Wrobel, S, Editor
Affiliations:
-
Publ. Info: New York, NY, USA : ACM Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 1036 - 1043 Identifier: ISBN: 1-59593-180-5