Semi-supervised Learning on Directed Graphs
2005
Conference Paper
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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.
Author(s): | Zhou, D. and Schölkopf, B. and Hofmann, T. |
Book Title: | Advances in Neural Information Processing Systems 17 |
Journal: | Advances in Neural Information Processing Systems |
Pages: | 1633-1640 |
Year: | 2005 |
Month: | July |
Day: | 0 |
Editors: | LK Saul and Y Weiss and L Bottou |
Publisher: | MIT Press |
Department(s): | Empirical Inference |
Bibtex Type: | Conference Paper (inproceedings) |
Event Name: | 18th Annual Conference on Neural Information Processing Systems (NIPS 2004) |
Event Place: | Vancouver, BC, Canada |
Address: | Cambridge, MA, USA |
Digital: | 0 |
ISBN: | 0-262-19534-8 |
Organization: | Max-Planck-Gesellschaft |
School: | Biologische Kybernetik |
Links: |
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BibTex @inproceedings{2781, title = {Semi-supervised Learning on Directed Graphs}, author = {Zhou, D. and Sch{\"o}lkopf, B. and Hofmann, T.}, journal = {Advances in Neural Information Processing Systems}, booktitle = {Advances in Neural Information Processing Systems 17}, pages = {1633-1640}, editors = {LK Saul and Y Weiss and L Bottou}, publisher = {MIT Press}, organization = {Max-Planck-Gesellschaft}, school = {Biologische Kybernetik}, address = {Cambridge, MA, USA}, month = jul, year = {2005}, doi = {}, month_numeric = {7} } |