Cross-validation optimization for large scale hierarchical classification kernel methods

Advances in Neural Information Processing Systems

Volumn , Issue , 2007, Pages 1233-1240

  Seeger, Matthias W ^a  

^a MAX PLANCK INSTITUTE FOR BIOLOGICAL CYBERNETICS   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

CLASS STRUCTURES; CROSS VALIDATION; HIERARCHICAL CLASSIFICATION; KERNEL METHODS; KERNEL PARAMETER; LOG LIKELIHOOD; MULTI-CLASS; TEXT CLASSIFICATION;

EID: 46249109879     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (12)

1. L. Cai and T. Hofmann. Hierarchical document categorization with support vector machines. In CIKM 13, pages 78-87, 2004.
2. K. Crammer and Y. Singer. On the algorithmic implementation of multiclass kernel-based vector machines. J. M. Learn. Res., 2:265-292, 2001.
3. P. Craven and G. Wahba. Smoothing noisy data with spline functions: Estimating the correct degree of smoothing by the method of generalized cross-validation. Numerische Mathematik, 31:377-403, 1979.
4. P.J. Green and B. Silverman. Nonparametric Regression and Generalized Linear Models. Monographs on Statistics and Probability. Chapman & Hall, 1994.
5. C.-W. Hsu and C.-J. Lin. A comparison of methods for multi-class support vector machines. IEEE Transactions on Neural Networks, 13:415-425, 2002.
6. Y. Qi, T. Minka, R. Picard, and Z. Ghahramani. Predictive automatic relevance determination by expectation propagation. In Proceedings of ICML 21, 2004.
7. M. Seeger. Gaussian processes for machine learning. International Journal of Neural Systems, 14(2):69-106, 2004.
8. M. Seeger. Cross-validation optimization for structured Hessian kernel methods. Technical report, Max Planck Institute for Biologic Cybernetics, Tübingen, Germany, 2006. See www.kyb.tuebingen.mpg.de/bs/people/seeger.
9. Y. Shen, A. Ng, and M. Seeger. Fast Gaussian process regression using KD-trees. In Advances in NIPS 18, 2006.
10. A. Smola and P. Bartlett. Sparse greedy Gaussian process regression. In Advances in NIPS 13, pages 619-625, 2001.
11. C. K. I. Williams and D. Barber. Bayesian classification with Gaussian processes. IEEE PAMI, 20(12):1342-1351, 1998.
12. C. Yang, R. Duraiswami, and L. Davis. Efficient kernel machines using the improved fast Gauss transform. In Advances in NIPS 17, pages 1561-1568, 2005.