Supervised encoding of graph-of-graphs for classification and regression problems

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

Volumn 6203 LNCS, Issue , 2010, Pages 449-461

  Zhang, Shu Jia ^a   Hagenbuchner, Markus ^a   Scarselli, Franco ^c   Tsoi, Ah Chung ^b  

^a UNIVERSITY OF WOLLONGONG   (Australia)

^b HONG KONG BAPTIST UNIVERSITY   (Hong Kong)

^c UNIVERSITY OF SIENA   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION PERFORMANCE; DATA-DRIVEN METHODS; ENCODING OF GRAPHS; FINITE DEPTH; GENERAL CLASS; MACHINE-LEARNING; REAL-WORLD PROBLEM; REGRESSION PROBLEM; STRUCTURED INFORMATION; STRUCTURED PROBLEMS; MACHINE LEARNING APPROACHES;

MARKUP LANGUAGES; XML; ARTIFICIAL INTELLIGENCE; GRAPHIC METHODS; LEARNING SYSTEMS;

GRAPHIC METHODS; GRAPH THEORY;

EID: 77955334635     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-14556-8_45     Document Type: Conference Paper

References (14)

1. Gori, M., Küchler, A., Soda, G.: On the implementation of frontier-to-root tree automata in recursive neural networks. IEEE Transactions on Neural Networks 10 (6), 1305-1314 (1999)
2. Frasconi, P., Francesconi, E., Gori, M., Marinai, S., Sheng, J., Soda, G.: Logo recognition by recursive neural networks. In: Kasturi, R., Tombre, K. (eds.) GREC 1997. LNCS, vol. 1389, pp. 104-117. Springer, Heidelberg (1998)
3. Sperduti, A., Majidi, D., Starita, A.: Extended cascade-correlation for syntactic and structural pattern recognition. In: Perner, P., Rosenfeld, A., Wang, P. (eds.) SSPR 1996. LNCS, vol. 1121, pp. 90-99. Springer, Heidelberg (1996)
4. Hammer, B., Micheli, A., Sperduti, A.: Universal approximation capability of cascade correlation for structures. Neural Computation 17 (5), 1109-1159 (2005)
5. Bianucci, A., Micheli, A., Sperduti, A., Starita, A.: Quantitative structure-activity relationships of benzodiazepines by recursive cascade correlation. In: IEEE Processing of IJCNN 1998- IEEE World Congress on Computational Intelligence, Anchorage, Alaska, May 1998, pp. 117-122 (1998)
6. Scarselli, F., Gori, M., Tsoi, A., Hagenbuchner, M., Monfardini, G.: Computational capabilities of graph neural networks. IEEE Transactions on Neural Networks 20 (1), 81-102 (2009)
7. Yong, S., Hagenbuchner, M., Tsoi, A., Scarselli, F., Gori, M.: Document mining using graph neural network. In: Fuhr, N., Lalmas, M., Trotman, A. (eds.) INEX 2006. LNCS, vol. 4518, pp. 458-472. Springer, Heidelberg (2007)
8. KC, M., Hagenbuchner, M., Tsoi, A., Scarselli, F., Gori, M., Sperduti, S.: Xml document mining using contextual self-organizing maps for structures. LNCS. Springer, Heidelberg (2007)
9. Hagenbuchner, M., Sperduti, A., Tsoi, A.: A self-organizing map for adaptive processing of structured data. IEEE Transactions on Neural Networks 14 (3), 491-505 (2003)
10. Zhang, S., Hagenbuchner, M., Tsoi, A., Sperduti, A.: Self Organizing Maps for the clustering of large sets of labeled graphs. Springer, Heidelberg (2009)
11. Mihalcea, R., Tarau, P.: TextRank: Bringing order into texts. In: Proceedings of EMNLP- 04 and the 2004 Conference on Empirical Methods in Natural Language Processing (July 2004)
12. The rainbow software package, http://www.cs.umass.edu/~mccallum/bow/ rainbow/
13. Chau, R., Tsoi, A. C., Hagenbuchner, M., Lee, V.: A conceptlink graph for text structure mining. In: Mans, B. (ed.) Thirty-Second Australasian Computer Science Conference (ACSC 2009), Wellington, New Zealand. CRPIT, vol. 91, pp. 129-137. ACS (2009)
14. Sontag, E.: Capabilities and training of feedforward nets. In: Neural networks, New Brunswick, NJ, Boston, MA. Academic Press, London (1990)