Kernel methods for graphs: A comprehensive approach

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

Volumn 5178 LNAI, Issue PART 2, 2008, Pages 662-669

  Camastra, Francesco ^a   Petrosino, Alfredo ^a  

^a UNIVERSITY OF NAPLES PARTHENOPE   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

KNOWLEDGE BASED SYSTEMS; MACHINE LEARNING;

KERNEL METHODS; MACHINE LEARNING TECHNOLOGY; STRUCTURED DATA; VECTORIAL DATA;

LEARNING ALGORITHMS;

EID: 57749195278     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-85565-1_82     Document Type: Conference Paper

References (17)

1. Frasconi, P., Gori, M., Sperduti, A.: A general framework for adaptive processing of data sequences. IEEE transactions on Neural Networks 9(5), 768-786 (1997)
2. Hagenbuchner, M., Sperduti, A., Tsoi, A.: A self-organizing map for adaptive processing of structured data. IEEE transactions on Neural Networks 14(23), 491-505 (2003)
3. Shawe-Taylor, J., Cristianini, N.: Kernels Methods for Pattern Analysis. Cambridge University Press, Cambridge (2004)
4. Gärtner, T.: A survey of kernels for structured data. SIGKDD Explorations 5(1), 49-58 (2003)
5. Berg, C., Christensen, J., Ressel, P.: Harmonic analysis on semigroups. Springer, New York (1984)
6. Gärtner, T., Flach, P., Wrobel, S.: On graph kernels: Hardness results and efficient alternatives. In: Proceedings of 16th Annual Conference on Computational Learning Theory and 7th Kernel Workshop, pp. 129-143. IEEE Press, Los Alamitos (2003)
7. Rasmussen, C., Williams, C.: Gaussian Processes for Machine Learning. MIT Press, Cambridge (2006)
8. Gärtner, T., Driessens, K., Ramon, J.: Graph kernels and gaussian processes for relational reinforcement learning. In: Horváth, T., Yamamoto, A. (eds.) ILP 2003. LNCS (LNAI), vol. 2835, pp. 146-163. Springer, Heidelberg (2003)
9. Gartner, T., Lloyd, J., Flach, P.: Kernels for structured data. In: Matwin, S., Sammut, C. (eds.) ILP 2002. LNCS (LNAI), vol. 2583, pp. 66-83. Springer, Heidelberg (2003)
10. Kashima, H., Tsuda, K., Inokuchi, A.: Marginalized kernels between labeled graphs. In: Proceedings of 10th International Conference on Machine Learning, pp. 321-328. IEEE Press, Los Alamitos (2003)
11. Tsuda, K., Kin, T., Asai, K.: Marginalized kernels for biological sequences. Bioinformatics 18, S268-S275 (2002)
12. Mahé, P., Ueda, N., Akutsu, T., Perret, J.L., Vert, J.P.: Extensions of marginalized graph kernels. In: Proceedings of 21st International Conference on Machine Learning (ICML 2004), pp. 552-559. IEEE Press, Los Alamitos (2004)
13. Horvath, T., Gärtner, T., Flach, P., Wrobel, S.: Cyclic pattern kernels for predictive graph mining. In: Proceedings of KDD 2004, pp. 158-167. ACM Press, New York (2004)
14. Zaki, M.: Efficiently mining frequent trees in a forest. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 71-80. ACM Press, New York (2002)
15. Tarjan, R.: Depth-first search and linear graphs algorithms. SIAM Journal on Computing 1(2), 146-160 (1972)
16. Read, R., Tarjan, R.: Bounds on backtrack algorithms for listing cycles, paths and spanning trees. Networks 5(3), 237-252 (1975)
17. Filippone, M., Camastra, F., Masulli, F., Rovetta, S.: A survey of kernel and spectral method for clustering. Pattern Recognition 41(1), 174-192 (2008)