PAC-Bayesian Generalization Bound for Density Estimation with Application to Co-clustering
2009
Conference Paper
ei
We derive a PAC-Bayesian generalization bound for density estimation. Similar to the PAC-Bayesian generalization bound for classification, the result has the appealingly simple form of a tradeoff between empirical performance and the KL-divergence of the posterior from the prior. Moreover, the PAC-Bayesian generalization bound for classification can be derived as a special case of the bound for density estimation. To illustrate a possible application of our bound we derive a generalization bound for co-clustering. The bound provides a criterion to evaluate the ability of co-clustering to predict new co-occurrences, thus introducing a supervised flavor to this traditionally unsupervised task.
Author(s): | Seldin, Y. and Tishby, N. |
Book Title: | JMLR Workshop and Conference Proceedings Volume 5: AISTATS 2009 |
Journal: | In the proceedings of the 12th International Conference on Artificial Intelligence and Statistics (AISTATS 2009) |
Pages: | 472-479 |
Year: | 2009 |
Month: | April |
Day: | 0 |
Publisher: | MIT Press |
Department(s): | Empirical Inference |
Bibtex Type: | Conference Paper (inproceedings) |
Event Name: | 12th International Conference on Artificial Intelligence and Statistics |
Event Place: | Clearwater Beach, FL, USA |
Address: | Cambridge, MA, USA |
Digital: | 0 |
Language: | en |
Organization: | Max-Planck-Gesellschaft |
School: | Biologische Kybernetik |
Links: |
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BibTex @inproceedings{6592, title = {PAC-Bayesian Generalization Bound for Density Estimation with Application to Co-clustering}, author = {Seldin, Y. and Tishby, N.}, journal = {In the proceedings of the 12th International Conference on Artificial Intelligence and Statistics (AISTATS 2009)}, booktitle = {JMLR Workshop and Conference Proceedings Volume 5: AISTATS 2009}, pages = {472-479}, publisher = {MIT Press}, organization = {Max-Planck-Gesellschaft}, school = {Biologische Kybernetik}, address = {Cambridge, MA, USA}, month = apr, year = {2009}, doi = {}, month_numeric = {4} } |