Predictive learning with structured (grouped) data

Neural Networks

Volumn 22, Issue 5-6, 2009, Pages 766-773

  Liang, Lichen ^a   Cai, Feng ^a   Cherkassky, Vladimir ^a  

^a University of Minnesota   (United States)

Author keywords

Heterogeneous data; Learning with structured data; Model selection; Multi task learning; SVM; SVM Plus

Indexed keywords

HETEROGENEOUS DATA; LEARNING WITH STRUCTURED DATA; MODEL SELECTION; MULTI-TASK LEARNING; SVM; SVM-PLUS;

CLASSIFIERS; ESTIMATION; SUPPORT VECTOR MACHINES;

EDUCATION;

ARTICLE; BREAST CANCER; CLASSIFIER; HEART DISEASE; HEPATITIS; MACHINE LEARNING; MATHEMATICAL ANALYSIS; METHODOLOGY; PREDICTION; PRIORITY JOURNAL; STANDARD; STATISTICAL MODEL; SUPPORT VECTOR MACHINE;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BREAST NEOPLASMS; DATABASES, FACTUAL; FEMALE; FORECASTING; HEART DISEASES; HEPATITIS; HUMANS; LINEAR MODELS; MALE;

EID: 68149163567     PISSN: 08936080     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neunet.2009.06.030     Document Type: Article

References (23)

1. Ando R., and Zhang T. A Framework for Learning predictive structures from multiple tasks and unlabeled data. Journal of Machine Learning Research (2005)
2. Argyriou, A., Evgeniou, T., & Pontil, M. (2006). Multi-task feature learning. In NIPS
3. Bakker B., and Heskes T. Task clustering and gating for Bayesian multitask learning. Journal of Machine Learning Research (2004)
4. Ben-David, S., Gehrke, J., & Schuller, R. (2002). A theoretical framework for learning from a pool of disparate data sources. In ACM KDD
5. Ben-David, S., & Schuller, R. (2003). Exploiting task relatedness for multiple task learning. In Proceedings of computational learning theory
6. Cai, F., & Cherkassky, V. (2009). SVM+ regression and multi-task learning. In IJCNN
7. In: Camps-Valls G., Rojo-Alvarez J.L., and Martinez-Ramon M. (Eds). Kernel methods in bioengineering, signal and image processing (2007), Idea Group Publishing, London
8. Caruana R. Multi-task learning. Machine Learning 28 (1997) 41-75
9. Cherkassky V., and Mulier F. Learning from data. second edition (2007), John Wiley & Sons, New York
10. Evgeniou, T., & Pontil, M. (2004). Regularized multi-task learning. In Proc. 17th SIGKDD conf. on knowledge discovery and data mining
11. Lawrence, N. D., & Platt, J. C. (2004). Learning to learn with the informative vector machine. In ICML
12. Liang, L., & Cherkassky, V. (2008). Connection between SVM+ and multi-task learning. In IJCNN
13. Liang, L., & Cherkassky, V. (2007). Learning using structured data: Application to fMRI data analysis. In IJCNN
14. Liang, L. (2008). Application and development of new learning methodologies for fMRI data analysis. Ph.D. Thesis, Department of ECE, University of Minnesota
15. Liao, X. J., & Carin, L. (2005). Radial basis function network for multi-task learning. In NIPS
16. Momma M., and Bennett K.P. Constructing orthogonal latent features for arbitrary loss. In: Nikravesh, Guyon, Gunn, and Zadeh (Eds). Feature extraction: Foundations and applications (2006), Springer
17. Obozinski, G., Taskar, B., & Jordan, M. (2006). Multi-task feature selection. In Technical report
18. Raina, R., Ng, Andrew Y., & Koller, D. (2006). Constructing informative priors using transfer learning. In ICML
19. Simon R., Radmacher M.D., Dobbin K., and McShane L.M. Pitfalls in the use of DNA microarray data for diagnostic and prognostic classification. Journal of the National Cancer Institute 95 1 (2003) 14-18
20. Vapnik, V., Akshay Vashist, A., & Pavlovitch, N. (2009). Learning using hidden information (Learning with teacher). In Proc. IJCNN
21. Vapnik V.N. Statistical learning theory (1998), Wiley, New York
22. Vapnik V.N. Empirical inference science afterword of 2006 (2006), Springer
23. Vapnik V.N. Estimation of dependencies based on empirical data (1982), Springer Verlag, New York