O Papel da Inferência Seletiva em Aprendizado de Máquina
Resumo
Um dos desafios em aprendizado de máquina é a automatização de processos de inferência empírica. Neste artigo é apresentado um tipo de inferência ainda não explorado em aprendizado de máquina, denominado inferência seletiva. Apesar de algoritmos de inferência mais gerais poderem ser empregados na realização de inferência seletiva, é defendida a ideia de que um algoritmo para realizá-la diretamente pode ter sucesso mesmo quando algoritmos de inferência mais gerais falham. Para corroborar essa ideia, são relatados experimentos iniciais conduzidos em bases de dados textuais.
Referências
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Burges, C. J. C. (1998). A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 2(2):121–167.
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Joachims, T. (1999). “Transductive inference for text classification using support vector machines”. Em ICML ’99: Proceedings of the 16th International Conference on Machine Learning, páginas 200–209.
Joachims, T. (2002). “Optimizing search engines using clickthrough data”. Em KDD ’02: Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, páginas 133–142.
Joachims, T. (2003). “Transductive learning via spectral graph partitioning”. Em ICML ’03: Proceedings of the 20th International Conference on Machine Learning, páginas 290–297.
Rosenblatt, F. (1957). The perceptron: a perceiving and recognizing automaton. Relatório Técnico 85-460-1, Cornell Aeronautical Laboratory.
Vapnik, V. (1995). The Nature of Statistical Learning Theory. Springer-Verlag. Vapnik, V. (2006). Estimation of Dependences Based on Empirical Data. Information Science and Statistics. Springer-Verlag, 2a edição.
Weston, J., Perez-Cruz, F., Bousquet, O., Chapelle, O., Elisseeff, A., e Scholkopf, B. (2003). Feature selection and transduction for prediction of molecular bioactivity for drug design. Bioinformatics, 19(6):764–771.
Alpaydin, E. (2004). Introduction to Machine Learning. Adaptive Computation and Machine Learning. The MIT Press.
Audibert, J. Y. (2008). Transductive learning and computer vision. NIPS 2008 Workshop on Learning with Data-dependent Concept Spaces (abstract and invited talk).
Burges, C. J. C. (1998). A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 2(2):121–167.
Campos, L. F. B. (2007). Increase of precision on the top of the list of retrieved web documents using global and local link analysis. Webology, 4(3). [link].
El-Yaniv, R., Pechyony, D., e Vapnik, V. (2008). Large margin vs. large volume in transductive learning. Machine Learning, 72(3):173–188.
Joachims, T. (1999). “Transductive inference for text classification using support vector machines”. Em ICML ’99: Proceedings of the 16th International Conference on Machine Learning, páginas 200–209.
Joachims, T. (2002). “Optimizing search engines using clickthrough data”. Em KDD ’02: Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, páginas 133–142.
Joachims, T. (2003). “Transductive learning via spectral graph partitioning”. Em ICML ’03: Proceedings of the 20th International Conference on Machine Learning, páginas 290–297.
Rosenblatt, F. (1957). The perceptron: a perceiving and recognizing automaton. Relatório Técnico 85-460-1, Cornell Aeronautical Laboratory.
Vapnik, V. (1995). The Nature of Statistical Learning Theory. Springer-Verlag. Vapnik, V. (2006). Estimation of Dependences Based on Empirical Data. Information Science and Statistics. Springer-Verlag, 2a edição.
Weston, J., Perez-Cruz, F., Bousquet, O., Chapelle, O., Elisseeff, A., e Scholkopf, B. (2003). Feature selection and transduction for prediction of molecular bioactivity for drug design. Bioinformatics, 19(6):764–771.
Publicado
19/07/2011
Como Citar
BRAGA, Ígor Assis; MONARD, Maria Carolina.
O Papel da Inferência Seletiva em Aprendizado de Máquina. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 8. , 2011, Natal/RN.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2011
.
p. 584-595.
ISSN 2763-9061.
