Multi-instance learning using recurrent neural networks

Proceedings of the International Joint Conference on Neural Networks

Volumn , Issue , 2012, Pages

  D'avila Garcez, A S ^a   Zaverucha, G ^b  

^a CITY UNIVERSITY   (United Kingdom)

^b FEDERAL UNIVERSITY OF RIO DE JANEIRO   (Brazil)

Author keywords

Multiple Instance Learning; Neural Symbolic Integration; Recurrent Networks; Structured Learning

Indexed keywords

CONNECTIONIST APPROACH; DATA SETS; MULTI-INSTANCE LEARNING; MULTIPLE INSTANCE LEARNING; NEURAL-SYMBOLIC INTEGRATION; RECURRENT NETWORKS; RECURRENT NEURAL NETWORK MODEL; STRUCTURED LEARNING; TRAINING SETS;

ARTIFICIAL INTELLIGENCE; LEARNING SYSTEMS;

RECURRENT NEURAL NETWORKS;

EID: 84865094983     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IJCNN.2012.6252784     Document Type: Conference Paper

References (21)

1. T. G. Dietterich, R. H. Lathrop, and T. Lozano-Perez, "Solving the multiple instance problem with axis-parallel rectangles," Artificial Intelligence, vol. 89, no. 1-2, pp. 31-71, 1997.
2. J. Foulds and E. Frank, "A review of multi-instance learning assumptions," The Knowledge Engineering Review, vol. 25, pp. 1-25, 2010.
3. Q. Zhang and S. A. Goldman, "Em-dd: An improved multiple-instance learning technique," in In Advances in Neural Information Processing Systems. MIT Press, 2001, pp. 1073-1080.
4. J. Wang, "Solving the multiple-instance problem: A lazy learning approach," in In Proc. 17th International Conf. on Machine Learning. Morgan Kaufmann, 2000, pp. 1119-1125.
5. J. Ramon and L. D. Raedt, "Multi instance neural networks," in Proceedings of the ICML-2000 workshop on attribute-value and relational learning, 2000, pp. 53-60, uRL: http://www.cs.kuleuven.ac.be/cgi-bindtai/publ- info.pl?id=31670.
6. Z. hua Zhou and M. ling Zhang, "Neural networks for multi-instance learning," In: Proceedings of the International Conference on Intelligent Information Technology, Tech. Rep., 2002.
7. D. Rumelhart, G. Hinton, and R. Williams, "Learning internal representations by error propagation," in Parallel Distributed Processing: Explorations in the Microstructure of Cognition, D. Rumelhart andJ. McClelland, Eds. MIT Press, 1986, vol. 1, pp. 318-362.
8. L. Valiant, "Knowledge infusion: In pursuit of robustness in artificial intelligence," in Proceedings of the 28th Conference on Foundations of Software Technology and Theoretical Computer Science, Bangalore, India, 2008, pp. 415-422.
9. A. d'Avila Garcez, K. Broda, and D. Gabbay, Neural-Symbolic Learning Systems: Foundations and Applications, ser. Perspectives in Neural Computing. Springer, 2002.
10. A. d'Avila Garcez, L. Lamb, and D. Gabbay, Neural-Symbolic Cognitive Reasoning, ser. Cognitive Technologies. Springer, 2009.
11. R. Basilio, G. Zaverucha, and A. d'Avila Garcez, "Inducing relational concepts with neural networks via the LINUS system," in Proceedings of the Fifth International Conference on Neural Information Processing ICONIP'98, 1998, pp. 1507-1510.
12. N. Lavrac, S. Dzeroski, and M. Grobelnik, "Experiments in learning nonrecursive definitions of relations with LINUS," Josef Stefan Institute, Yugoslavia, Tech. Rep., 1990.
13. B. Kijsirikul and T. Lerdlamnaochai, "First-order logical neural networks," Int. J. Hybrid Intell. Syst., vol. 2, no. 4, pp. 253-267, 2005.
14. A. d'Avila Garcez and G. Zaverucha, "The connectionist inductive learning and logic programming system," Applied Intelligence Journal, Special Issue on Neural Networks and Structured Knowledge, vol. 11, no. 1, pp. 59-77, 1999.
15. R. Basilio, G. Zaverucha, and V. Barbosa, "Learning logic programs with neural networks," in Inductive Logic Programming. Springer LNAI 2157, 2001, pp. 15-26.
16. W. Uwents and H. Blockeel, "Classifying relational data with neural networks," in Proceedings of 15th International Conference on Inductive Logic Programming, ser. Lecture Notes in Computer Science, S. Kramer and B. Pfahringer, Eds., vol. 3625. Bonn, Germany: Springer, 2005, pp. 384-396.
17. M. Jordan, "Attractor dynamics and parallelisms in a connectionist sequential machine," in Proceedings of the Eighth Annual Conference of the Cognitive Science Society, 1986, pp. 531-546.
18. O. Maron and T. Lozano-Perez, "A framework for multiple-instance learning," in Neural Information Processing Systems 10. Cambridge, MA: MIT Press, 1997.
19. W. Uwents, G. Monfardini, H. Blockeel, M. Gori, and F. Scarselli, "Neural networks for relational learning: an experimental comparison," Machine Learning, vol. 82, no. 3, pp. 315-349, 2011.
20. L. Lamb, R. Borges, and A. d'Avila Garcez, "A connectionist cognitive model for temporal synchronisation and learning," in Proceedings of the Twenty-Second AAAI Conference on Artificial Intelligence AAAI 2007. AAAI Press, 2007, pp. 827-832.
21. Z. Zhou and J. Xu, "On the relation between multi-instance learning and semi-supervised learning," in ICML '07: Proceedings of the 24th international conference on Machine learning. New York, NY, USA: ACM, 2007, pp. 1167-1174.