Learning from Labeled and Unlabeled Data on a Directed Graph
2005
Conference Paper
ei
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.
Author(s): | Zhou, D. and Huang, J. and Schölkopf, B. |
Book Title: | Proceedings of the 22nd International Conference on Machine Learning |
Pages: | 1041 -1048 |
Year: | 2005 |
Month: | August |
Day: | 0 |
Editors: | L De Raedt and S Wrobel |
Publisher: | ACM |
Department(s): | Empirische Inferenz |
Bibtex Type: | Conference Paper (inproceedings) |
Event Name: | ICML 2005 |
Event Place: | Bonn, Germany |
Address: | New York, NY, USA |
Language: | en |
Organization: | Max-Planck-Gesellschaft |
School: | Biologische Kybernetik |
Links: |
PostScript
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BibTex @inproceedings{3463, title = {Learning from Labeled and Unlabeled Data on a Directed Graph}, author = {Zhou, D. and Huang, J. and Sch{\"o}lkopf, B.}, booktitle = {Proceedings of the 22nd International Conference on Machine Learning}, pages = {1041 -1048}, editors = {L De Raedt and S Wrobel}, publisher = {ACM}, organization = {Max-Planck-Gesellschaft}, school = {Biologische Kybernetik}, address = {New York, NY, USA}, month = aug, year = {2005}, doi = {}, month_numeric = {8} } |