Unbiased SVM density estimation with application to graphical pattern recognition

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

Volumn 4669 LNCS, Issue PART 2, 2007, Pages 271-280

  Trentin, Edmondo ^a   Di Iorio, Ernesto ^a  

^a UNIVERSITY OF SIENA   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION (OF INFORMATION); DATA STRUCTURES; GRAPHIC METHODS; PATTERN RECOGNITION;

ALGEBRAIC BINARY RELATIONS; GRAPHICAL PATTERN RECOGNITION;

SUPPORT VECTOR MACHINES;

EID: 38349045506     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-74695-9_28     Document Type: Conference Paper

References (14)

1. Bengio, Y., Simard, P., Frasconi, P.: Learning long-term dependencies with gradient descent is difficult (Special Issue on Recurrent Neural Networks, March 94). IEEE Transactions on Neural Networks 5(2), 157-166 (1994)
2. Bollobs, B.: Random Graphs, 2nd edn. Cambridge University Press, Cambridge, UK (2001)
3. Cassabaum, M.L., Waagen, D.E., Rodríguez, J.J., Schmitt, H.A.: Unsupervised optimization of support vector machine parameters. In: Sadjadi, F.A. (ed.) Proc. of SPIE, vol. 5426, pp. 316-325 (2004)
4. Di Massa, V., Monfardini, G., Sarti, L., Scarselli, F., Maggini, M., Gori, M.: A comparison between recursive neural networks and graph neural networks. World Congr. on Comp. Intelligence, 778-7825 (2006)
5. Duda, R.O., Hart, P.E.: Pattern Classification and Scene Analysis. Wiley, New York (1973)
6. Girolami, M., He, C.: Probability density estimation from optimally condensed data samples (2003)
7. Gori, M., Monfardini, G., Scarselli, F.: A new model for learning in graph domains. In: Proc. of IJCNN-05 (August 2005)
8. Hammer, B., Micheli, A., Sperduti, A.: Universal approximation capability of cascade correlation for structures. Neural Computation 17(5), 1109-1159 (2005)
9. Scholkopf, B., Platt, J.C., Shawe-Taylor, J.C., Smola, A.J., Williamson, R.C.: Estimating the support of a high-dimensional distribution. Neural Comput. 13(7), 1443-1471 (2001)
10. Smola, A.J., Scholkopf, B.: A tutorial on support vector regression. Statistics and Computing 14(3), 199-222 (2004)
11. Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714-735 (1997)
12. Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer, New-York (1995)
13. Watts, D., Strogatz, S.: Collective dynamics of small world networks. Nature 393, 440-442 (1998)
14. Weston, J., Gammerman, A., Stitson, M., Vapnik, V., Vovk, V., Watkins, C.: Support vector density estimation. In: Schölkopf, B., Burges, C.J.C, Smola, A.J. (eds.) Advances in Kernel Methods -Support Vector Learning, Cambridge, MA, pp. 293-306. MIT Press, Cambridge (1999)