Probabilistic joint feature selection for multi-task learning

Proceedings of the 7th SIAM International Conference on Data Mining

Volumn , Issue , 2007, Pages 332-342

  Xiong, Tao ^a   Bit, Jinbo ^b   Rao, Bharat ^b   Cherkassky, Vladimir ^a  

^a UNIVERSITY OF MINNESOTA   (United States)

^b SIEMENS CORPORATE RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DATA MINING; FEATURE EXTRACTION; REGRESSION ANALYSIS;

ADAPTIVE RIDGE REGRESSIONS; AUTOMATIC RELEVANCE DETERMINATION; FEATURE PATTERN; FEATURE SELECTION PROBLEM; MULTITASK LEARNING; PROBABILISTIC FRAMEWORK; SIMULTANEOUS MODEL; SYNTHETIC DATA;

LEARNING SYSTEMS;

EID: 70449089773     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1137/1.9781611972771.30     Document Type: Conference Paper

References (29)

1. R. K. Ando and T. Zhang, A Framework for learning predictive structures from multiple tasks and unlabeled data, Journal of Machine Learning Research, 2005.
2. S. G. Armato-III, M. L. Giger, and H. MacMahon. Automated detection of lung nodules in CT scans: preliminary results, Medical Physics, 28(8):1552 - 1561, 2001.
3. S. Boyd and L. Vandenberghe, Convex Optimization, Cambridge University Press, 2004.
4. R. Caruana, Multi-task learning, Machine Learning, 28, p. 41-75, 1997.
5. T. Evgeniou and M. Pontil, Regularized multi-task learning, Proceedings of the Tenth Conference on Knowledge Discovery and Data Mining, 2004.
6. Y. Gandvalet, Least absolute shrinkage is equivalent to quadratic penalization, In Proceedings, of the International Conference on Artificial Neural Networks, Perspectives in Neural Computing, pages 201-206, 1998.
7. Y. Gandvalet and S. Canu, Outcomes of the equivalence of adaptive ridge with least absolute shrinkage, In Advances in Neural Information Processing Systems, volume 11, MIT Press, 1999.
8. W. Greene, Econometric Analysis, Prentice Hall, fifth edition, 2002.
9. J. Goodman, Exponential priors for maximum entropy models. In Proceedings of the Annual Meeting of the Association for Computational Linguistics, In Proceedings of the Annual Meeting of the Association for Computational Linguistics, 2004.
10. I. Guyon and A. Ellisseeff, A introduction to viable and feature selection, Journal of Machine Learning Research, 3:1157-1182, 2003.
11. T. Hastie, R. Tibshirani and J. Friedman, The elements of Statistical Learning: Data Mining, Inference and Prediction, Springer-Verlag, 2001.
12. T. Heskes, Empirical Bayes for learning to learn, Proceedings of ICML-2000.
13. H. Liu and R. Setiono, Incremental feature selection, Applied Intelligence, 9(3):217:230, 1998.
14. T. Jebara, Multi-task feature and kernel selection for SVMs, In Proceedings of the Twenty-First International Conference on Machine learning (ICML), 2004.
15. S. Lee, H. Lee, P. Abbeel and A. Y. Ng. Efficient LI regularized logistic regression, In Proceedings of the Twenty-First National Conference on Artificial Intelligence (AAAI 2006).
16. D. P. Naidich, J. P. Ko, and J. Stoechek. Computer aided diagnosis: Impact on nodule detection amongst community level radiologist. A multi-reader study, In Proceedings of CARS 2004 Computer Assisted Radiology and Surgery, pages 902 - 907, 2004.
17. D. J. C. MacKay, Probable networks and plausible predictions - a review of practical Bayesian methods for supervised neural networks, Network: Computation in Neural Systems, vol. 6, pp. 469-505, 1995.
18. R. Malouf, A comparison of algorithms for maximum entropy parameter estimation, in Proceedings of the Sixth Conference on Natural Language Learning (CoNLL-2002).
19. T. P. Minka, A comparison of numerical optimizers for logistic regression, http://research.microsoft.com/̃minka/papers/logreg/.
20. G. Obozinski, B. Taskar and M. I. Jordan Multitask Feature Selection, Technical Report, UC Berkeley, 2006.
21. F. Provost and T. Fawcett, Robust Classification for Imprecise Environments, Machine Learning, vol. 42/3, pp. 203-231, 2001.
22. K. Suzuki, M. Kusumoto, S. Watanabe, R. Tsuchiya and H. Asamura, Radiologic classfication of small adenocarcinoma of the Lung: Radiologic-Pathologic Correlation and Its Prognostic Impact, The Annals of Thoracic Surgery CME Program, 81:413-20, 2006.
23. R. Tibshirani, Regression selection and shrinkage via the LASSO, Journal of the Royal Statistics Society Series B, 58(1):267-288,1996.
24. M. Tipping, The relevance vector machine, In Advances in Neural Information Processing Systems, volume 12, MIT Press, 2000.
25. M. Tipping, Sparse Bayesian learning and the relevance vector machine, Journal of Machine Learning Research, 1:211-244, 2001.
26. Y. Xue, X. Liao, L. Carin and B. Krishnapuram, Learning multiple classifiers with Dirichlet process mixture priors, In Workshop on Open Problems and Challenges for Nonparametric Bayesian Methods in Machine Learning at Neural Information Processing Systems, 2005.
27. K. Yu, V. Tresp, and A. Schwaighofer, Learning Gaussian Processes from Multiple Tasks, Proceedings of the 22nd International Conference on Machine Learning (ICML), 2005.
28. T. Zhang and F. Oles, Text categorization based on regularized linear classifiers, Information Retrieval, 4, 5-31.
29. J. Zhang, A Probabilistic framework for multi-task learning, Ph.D thesis, Carnegie Mellon University, 2006.