Kernel regression with order preferences

Proceedings of the National Conference on Artificial Intelligence

Volumn 1, Issue , 2007, Pages 681-686

  Zhu, Xiaojin ^a   Goldberg, Andrew B ^a  

^a University of Wisconsin Madison   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

LINEAR PROGRAMMING; OPTIMIZATION; PROBLEM SOLVING; RISK MANAGEMENT; SUPERVISED LEARNING; SUPPORT VECTOR MACHINES;

HOUSING PRICE PROBLEMS; KERNEL REGRESSION ALGORITHM; SENTIMENT ANALYSIS; STANDARD REGRESSION;

ALGORITHMS;

EID: 36349016616     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (23)

1. Belkin, M.; Niyogi, P.; and Sindhwani, V. 2004. Manifold regularizaron: A geometric framework for learning from examples. Technical Report TR-2004-06, University of Chicago.
2. Bi, J.; Bennett, K.; Embrechts, M.; Breneman, C.; and Song, M. 2003. Dimensionality reduction via sparse support vector machines. JMLR 3:1229-1243.
3. Bradley, P., and Mangasarian, O. 1998. Feature selection via concave minimization and support vector machines. In ICML98.
4. Brefeld, U.; Gaertner, T.; Scheffer, T.; and Wrobel, S. 2006. Efficient co-regularized least squares regression. In ICML06.
5. Burges, C.; Shaked, T.; Renshaw, E.; Lazier, A.; Deeds, M.; Hamilton, N.; and Hullender, G. 2005. Learning to rank using gradient descent. In ICML05.
6. Chu, W., and Ghahramani, Z. 2005. Gaussian processes for ordinal regression. JMLR 6(July):1019-1041.
7. Chu, W., and Keerthi, S. S. 2005. New approaches to support vector ordinal regression. In ICML05, 145-152.
8. Collobert, R.; Sinz, F.; Weston, J.; and Bottou, L. 2006. Large scale transductive SVMs. JMLR 7(Aug):1687-1712.
9. Dekel, O.; Manning, C.; and Singer, Y. 2004. Loglinear models for label-ranking. In NIPS 16.
10. Herbrich, R.; Obermayer, K.; and Graepel, T. 2000. Large margin rank boundaries for ordinal regression. In Smola, A. J.; Bartlett, P.; Schölkopf, B.; and Schuurmans, D., eds., Advances in Large Margin Classifiers. MIT Press. 115-132.
11. Joachims, T. 1999a. Making large-scale svm learning practical. In Schölkopf, B.; Burges, C.; and Smola, A., eds., Advances in Kernel Methods - Support Vector Learning. MIT Press.
12. Joachims, T. 1999b. Transductive inference for text classification using support vector machines. In ICML99, 200-209. Morgan Kaufmann, San Francisco, CA.
13. Joachims, T. 2002. Optimizing search engines using clickthrough data. In KDD02. ACM Press.
14. Kimeldorf, G., and Wahba, G. 1971. Some results on Tchebychean spline functions. Journal of Mathematics Analysis and Applications 33:82-95.
15. Mangasarian, O. L.; Shavlik, J. W.; and Wild, E. W. 2004. Knowledge-based kernel approximation. JMLR 5:1127-1141.
16. Mangasarian, O. 2000. Generalized support vector machines. In Smola, A. J.; Bartlett, P.; Schölkopf, B.; and Schuurmans, D., eds., Advances in Large Margin Classifiers. MIT Press. 135-146.
17. Mizra, M.; Sommers, J.; Barford, P.; and Zhu, X. 2007. A machine learning approach to TCP throughput prediction. In ACM SIGMETRICS.
18. Pang, B., and Lee, L. 2005. Seeing stars: exploiting class relationships for sentiment categorization with respect to rating scales. In Proceedings of the Association for Computational Linguistics.
19. Schölkopf, B.; Herbrich, R.; and Smola, A. J. 2001. A generalized representer theorem. In COLT.
20. Sindhwani, V.; Niyogi, P.; and Belkin, M. 2005. A co-regularized approach to semi-supervised learning with multiple views. In Proc. of the 22nd ICML Workshop on Learning with Multiple Views.
21. Smola, A., and Schölkopf, B. 2004. A tutorial on support vector regression. Statistics and Computing 14:199-222.
22. Yu, S.; Yu, K.; Tresp, V.; and Kriegel, H.-P. 2006. Collaborative ordinal regression. In ICML06.
23. Zhu, J.; Rosset, S.; Hastie, T.; and Tibshirani, R. 2004. 1-norm support vector machines. In NIPS 16.