Revisão de Teorias Relacionais Probabilísticas através de Exemplos com Invenção de Predicados
Resumo
A maioria dos algoritmos de aprendizado de máquina utiliza apenas o vocabulário fornecido explicitamente nos dados para construir os modelos. Entretanto, a extensão automática desse vocabulário com novas estruturas que representam informações implícitas sobre objetos do domínio pode enriquecer o aprendizado. Por outro lado, em alguns casos, já existe um modelo que é aproximadamente correto podendo a sua estrutura ser alterada o mínimo possível de forma a refletir corretamente a base de dados, caracterizando um caso particular de aprendizado denominado revisão de teoria. Este artigo, investiga os benefícios da extensão do vocabulário quando revisando modelos probabilísticos, tendo sido aplicada com sucesso em bases de dados artificiais e reais.Referências
De Raedt, L., Kersting, K., Kimmig, A., Revoredo, K., and Toivonen, H. (2008). Compressing probabilistic prolog programs. Machine Learning, 70(2-3):151–168.
De Raedt, L., Kimmig, A., and Toivonen, H. (2007). Problog: A probabilistic prolog and its application in link discovery. In Proc. IJCAI-2007, pages 2462–2467.
Friedman, N. (1997). Learning belief networks in the presence of missing values and hidden variables. In 14th Int. Conference on Machine Learning, pages 1252–133.
Friedman, N. (1998). The bayesian structural EM algorithm. In UAI, pages 129–138.
Kersting, K. and De Raedt, L. (2002). Basic principles of learning bayesian logic programs. Technical Report 174, Univ. of Freiburg, Inst. for Computer Science, German.
Kohavi, R. (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. In Proc. of the IJCAI, pages 1137–1145.
Kramer, S. (1995). Predicate invention: A comprehensive view. Technical Report ÖFAITR-95-32, Austrian Research Institute for Artificial Intelligence.
Mitchell, T. (1997). Machine Learning. McGraw-Hill, New York.
Muggleton, S. (1992). Inductive logic programming. McGraw-Hill, New York.
Nadeau, C. and Bengio, Y. (2003). Inference for the generalization error. Machine Learning, 52(3):239–281.
Paes, A., Revoredo, K., Zaverucha, G., and Costa, V. S. (2005a). Further experimental results of probabilistic first-order revision of theories from exampes. In 4th Workshop on Multi-Relational Data Mining / The Eleventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, page 59, Chicago, Illinois. ACM Press.
Paes, A., Revoredo, K., Zaverucha, G., and Costa, V. S. (2005b). Probabilistic first-order theory revision from examples. In Proceedings of the 15th International Conference on Inductive Logic Programming, volume 3625 of LNAI, pages 295–311. Springer.
Paes, A., Revoredo, K., Zaverucha, G., and Costa, V. S. (2006). Pforte: Revising probabilistic fol theories. In Proceedings of the 18th Brazilian AI Symposium (SBIA-06), volume 4140 of LNAI, pages 441–450. Springer.
Revoredo, K. (2009). Revisão de Teorias Relacionais Probabilísticas através de Exemplos com Invenção de Predicados. PhD thesis, COPPE-Sistemas/UFRJ. [link].
Revoredo, K., Paes, A., Zaverucha, G., and Costa, V. S. (2006). Combining predicate invention and revision of probabilistic fol theories. In Short paper proceedings of 16th International Conference on Inductive Logic Programming (ILP-06), pages 176–178.
Revoredo, K., Paes, A., Zaverucha, G., and Costa, V. S. (2007). Combinando invenção de predicados e revisão de teorias de primeira-ordem probabilísticas. In VI ENIA.
Revoredo, K., Paes, A., Zaverucha, G., and Costa, V. S. (2009). Revisando redes bayesianas através da introdução de variáveis não-observadas. In VII ENIA.
Srinivasan, A. (2001). The Aleph Manual.
De Raedt, L., Kimmig, A., and Toivonen, H. (2007). Problog: A probabilistic prolog and its application in link discovery. In Proc. IJCAI-2007, pages 2462–2467.
Friedman, N. (1997). Learning belief networks in the presence of missing values and hidden variables. In 14th Int. Conference on Machine Learning, pages 1252–133.
Friedman, N. (1998). The bayesian structural EM algorithm. In UAI, pages 129–138.
Kersting, K. and De Raedt, L. (2002). Basic principles of learning bayesian logic programs. Technical Report 174, Univ. of Freiburg, Inst. for Computer Science, German.
Kohavi, R. (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. In Proc. of the IJCAI, pages 1137–1145.
Kramer, S. (1995). Predicate invention: A comprehensive view. Technical Report ÖFAITR-95-32, Austrian Research Institute for Artificial Intelligence.
Mitchell, T. (1997). Machine Learning. McGraw-Hill, New York.
Muggleton, S. (1992). Inductive logic programming. McGraw-Hill, New York.
Nadeau, C. and Bengio, Y. (2003). Inference for the generalization error. Machine Learning, 52(3):239–281.
Paes, A., Revoredo, K., Zaverucha, G., and Costa, V. S. (2005a). Further experimental results of probabilistic first-order revision of theories from exampes. In 4th Workshop on Multi-Relational Data Mining / The Eleventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, page 59, Chicago, Illinois. ACM Press.
Paes, A., Revoredo, K., Zaverucha, G., and Costa, V. S. (2005b). Probabilistic first-order theory revision from examples. In Proceedings of the 15th International Conference on Inductive Logic Programming, volume 3625 of LNAI, pages 295–311. Springer.
Paes, A., Revoredo, K., Zaverucha, G., and Costa, V. S. (2006). Pforte: Revising probabilistic fol theories. In Proceedings of the 18th Brazilian AI Symposium (SBIA-06), volume 4140 of LNAI, pages 441–450. Springer.
Revoredo, K. (2009). Revisão de Teorias Relacionais Probabilísticas através de Exemplos com Invenção de Predicados. PhD thesis, COPPE-Sistemas/UFRJ. [link].
Revoredo, K., Paes, A., Zaverucha, G., and Costa, V. S. (2006). Combining predicate invention and revision of probabilistic fol theories. In Short paper proceedings of 16th International Conference on Inductive Logic Programming (ILP-06), pages 176–178.
Revoredo, K., Paes, A., Zaverucha, G., and Costa, V. S. (2007). Combinando invenção de predicados e revisão de teorias de primeira-ordem probabilísticas. In VI ENIA.
Revoredo, K., Paes, A., Zaverucha, G., and Costa, V. S. (2009). Revisando redes bayesianas através da introdução de variáveis não-observadas. In VII ENIA.
Srinivasan, A. (2001). The Aleph Manual.
Publicado
20/07/2010
Como Citar
REVOREDO, Kate; ZAVERUCHA, Gerson.
Revisão de Teorias Relacionais Probabilísticas através de Exemplos com Invenção de Predicados. In: CONCURSO DE TESES E DISSERTAÇÕES (CTD), 23. , 2010, Belo Horizonte/MG.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2010
.
p. 97-104.
ISSN 2763-8820.
