Kernel-based inductive transfer

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5212 LNAI, Issue PART 2, 2008, Pages 220-233

  Rückert, Ulrich ^a   Kramer, Stefan ^a  

^a TECHNICAL UNIVERSITY OF MUNICH   (Germany)

Author keywords

Inductive transfer; Kernels; Regularized risk minimization; Transfer learning

Indexed keywords

DATABASE SYSTEMS; EDUCATION; LEARNING SYSTEMS; ROBOT LEARNING;

CONVEX OPTIMIZATIONS; DATA SETS; INDUCTIVE TRANSFER; INDUCTIVE TRANSFERS; ITERATIVE PROCEDURES; KERNELS; LEARNING TASKS; LINEAR SPANS; META LEVELS; REGULARIZED RISK MINIMIZATION; SET-UPS; SMALL MOLECULES; TEXT DATUMS; TRANSFER LEARNING;

OPTIMIZATION;

EID: 56049099339     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-87481-2_15     Document Type: Conference Paper

References (24)

1. Argyriou, A., Evgeniou, T., Pontil, M.: Multi-task feature learning. In: Schölkopf, B., Platt, J.C., Hoffman, T. (eds.) Proceedings of the Twentieth Annual Conference on Neural Information Processing Systems 19. Advances in Neural Information Processing Systems, pp. 41-48. MIT Press, Cambridge (2006)
2. Baxter, J.: A model of inductive bias learning. Journal of Artificial Intelligence Research 12, 149-198 (2000)
3. Caruana, R.: Multitask learning. Machine Learning 28(1), 41-75 (1997)
4. Crammer, K., Keshet, J., Singer, Y.: Kernel design using boosting. In: Becker, S., Thrun, S., Obermayer, K. (eds.) Advances in Neural Information Processing Systems 15, pp. 537-544. MIT Press, Cambridge (2002)
5. Erhan, D., Bengio, Y., L'Heureux, P.-J., Yue, S.Y.: Generalizing to a zero-data task: a computational chemistry case study. Technical Report 1286, Département d'informatique et recherche opérationnelle, Université de Montréal (2006)
6. Evgeniou, T., Micchelli, C.A., Pontil, M.: Learning multiple tasks with kernel methods. J. Mach. Learn. Res. 6, 615-637 (2005)
7. Fang. H., Tong, W., Shi, L.M., Blair, R., Perkins, R., Branham, W. Hass. B.S., Xie, Q., Dial, S.L., Moland, C.L., Sheehan, D.M.: Structure-activity relationships for a large diverse set of natural, synthetic, and environmental estrogens. Chemical Research in Toxicology 14(3), 280-294 (2001)
8. Fontaine, F., Pastor, M., Zamora, I., Sanz, F.: Anchor-GRIND: Filling the gap between standard 3D QSAR and the grid-independent descriptors. Journal of Medicinal Chemistry 48(7), 2687-2694 (2005)
9. Gabrilovich, E., Markovitch, S.: Parameterized generation of labeled datasets for text categorization based on a hierarchical directory. In: Proceedings of The 27th Annual International ACM SIGIR Conference, Sheffield, UK, pp. 250-257. ACM Press, New York (2004)
10. Girolami, M., Rogers, S.: Hierarchic Bayesian models for kernel learning. In: ICML 2005: Proceedings of the 22nd international conference on Machine learning, pp. 241-248. ACM Press, New York (2005)
11. Helma, C., Cramer, T., Kramer, S., De Raedt, L.: Data mining and machine learning techniques for the identification of mutagenicity inducing substructures and structure activity relationships of noncongeneric compounds. Journal of Chemical Information and Computer Sciences 44(4), 1402-1411 (2004)
12. Hertz, T., Hillel, A.B., Weinshall, D.: Learning a kernel function for classification with small training samples. In: ICML 2006: Proceedings of the 23rd international conference on Machine learning, pp. 401-408. ACM, New York (2006)
13. Kaski, S.. Peltonen, J.: Learning from relevant tasks only. In: Kok, J.N., Koronacki, J., de Mántaras, R.L., Matwin, S., Mladenic, D., Skowron, A. (eds.) ECML 2007. LNCS (LNAI), vol. 4701, pp. 608-615. Springer, Heidelberg (2007)
14. Lanckriet, G.R.G., Cristianini, N., Bartlett, P., El Ghaoui, L., Jordan, M.I.: Learning the kernel matrix with semidefinite programming. J. Mach. Learn. Res. 5, 27-72 (2004)
15. Li, H., Yap, C.W., Ung, C.Y., Xue, Y., Cao, Z.W., Chen, Y.Z.: Effect of selection of molecular descriptors on the prediction of blood-brain barrier penetrating and nonpenetrating agents by statistical learning methods. Journal of Chemical Information and Modeling 45(5), 1376-1384 (2005)
16. Micchelli, C.A., Pontil, M.: Learning the kernel function via regularization. J. Mach. Learn. Res. 6, 1099-1125 (2005)
17. Ong, C.S., Smola, A.J., Williamson, R.C.: Learning the kernel with hyperkernels. J. Mach. Learn. Res. 6, 1043-1071 (2005)
18. Pfahringer, B., Bensusan, H., Giraud-Carrier, C.G.: Meta-learning by landmarking various learning algorithms. In: ICML 2000: Proceedings of the Seventeenth International Conference on Machine Learning, pp. 743-750. Morgan Kaufmann, San Francisco (2000)
19. Sonnenburg, S., Rätsch, G., Schäfer, C., Schölkopf, B.: Large scale multiple kernel learning. J. Mach. Learn. Res. 7, 1531-1565 (2006)
20. Wegner, J.K., Fröhlich, H., Zell, A.: Feature selection for descriptor based classification models. 1. theory and ga-sec algorithm. Journal of Chemical Information and Modeling 44(3), 921-930 (2004)
21. Weston, J., Herbrich, R.: Adaptive margin support vector machines. In: Advances in Large-Margin Classifiers, pp. 281-295. MIT Press, Cambridge (2000)
22. Xue, Y., Liao, X., Carin, L., Krishnapuram, B.: Multi-task learning for classification with dirichlet process priors. J. Mach. Learn. Res. 8, 35-63 (2007)
23. Yoshida, F., Topliss, J.: QSAR model for drug human oral bioavailability. J. Med. Chem. 43, 2575-2585 (2000)
24. Yu. K., Tresp, V., Schwaighofer, A.: Learning gaussian processes from multiple tasks. In: ICML 2005: Proceedings of the 22nd international conference on Machine learning, pp. 1012-1019. ACM Press, New York (2005)