Uma formulação para a máquina de aprendizagem mínima baseada em programação linear
Resumo
A máquina de aprendizagem mínima (MLM) é um método de aprendizado supervisionado que consiste na utilização de um mapeamento linear entre distâncias dos espaços de entrada e saída, seguido de um processo de otimização para, a partir das distâncias estimadas, estimar a saída. A etapa de teste da MLM envolve a resolução de um problema de otimização não-convexo, e pode sofrer com problemas associados a mínimos locais. Com isso em vista, neste artigo e apresentada uma formulação nessa etapa utilizando programação linear. Os experimentos mostram que o método proposto atinge desempenho semelhante aquele obtido com o algoritmo original, adicionalmente produzindo resultados com menor variância.
Referências
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Lenz, I., Lee, H., and Saxena, A. (2015). Deep learning for detecting robotic grasps. International Journal of Robotics Research (IJRR) Special Issue on Robot Vision.
Sainath, T., Kingsbury, B., Saon, G., Soltau, H., rahman Mohamed, A., Dahl, G., and Ramabhadran, B. (2015). Deep convolutional neural networks for large-scale speech tasks. Neural Networks, 64:39-48.
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., Dieleman, S., Grewe, D., Nham, J., Kalchbrenner, N., Sutskever, I., Lillicrap, T., Leach, M., Kavukcuoglu, K., Graepel, T., and Hassabis, D. (2016). Mastering the game of go with deep neural networks and tree search. Nature, 529:484-503.
SOUZA JUNIOR, A. H., Corona, F., Miché, Y., Lendasse, A., Barreto, G., and Simula, O. (2013). Minimal learning machine: A new distance-based method for supervised learning. In Proceedings of the 12th International Work Conference on Artificial Neural Networks (IWANN'2013), volume 7902 of Lecture Notes in Computer Science, pages 408-416. Springer.
Souza Júnior, A. H., Corona, F., Barreto, G. A., Miche, Y., and Lendasse, A. (2015). Minimal learning machine: A novel supervised distance-based approach for regression and classification. Neurocomputing, 164:34 - 44.
Bishop, C. M. et al. (2006). Pattern recognition and machine learning, volume 1. Springer New York.
Hastie, T., Tibshirani, R., and Friedman, J. (2001). Elements of Statistical Learning: Data Mining, Inference and Prediction. Springer.
Hinton, G. E. and Salakhutdinov, R. R. (2006). Reducing the dimensionality of data with neural networks. Science, 313(5786):504-507.
Krizhevsky, A., Sutskever, I., and Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Pereira, F., Burges, C. J. C., Bottou, L., and Weinberger, K. Q., editors, Advances in Neural Information Processing Systems 25, pages 1097-1105. Curran Associates, Inc.
Lenz, I., Lee, H., and Saxena, A. (2015). Deep learning for detecting robotic grasps. International Journal of Robotics Research (IJRR) Special Issue on Robot Vision.
Sainath, T., Kingsbury, B., Saon, G., Soltau, H., rahman Mohamed, A., Dahl, G., and Ramabhadran, B. (2015). Deep convolutional neural networks for large-scale speech tasks. Neural Networks, 64:39-48.
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., Dieleman, S., Grewe, D., Nham, J., Kalchbrenner, N., Sutskever, I., Lillicrap, T., Leach, M., Kavukcuoglu, K., Graepel, T., and Hassabis, D. (2016). Mastering the game of go with deep neural networks and tree search. Nature, 529:484-503.
SOUZA JUNIOR, A. H., Corona, F., Miché, Y., Lendasse, A., Barreto, G., and Simula, O. (2013). Minimal learning machine: A new distance-based method for supervised learning. In Proceedings of the 12th International Work Conference on Artificial Neural Networks (IWANN'2013), volume 7902 of Lecture Notes in Computer Science, pages 408-416. Springer.
Souza Júnior, A. H., Corona, F., Barreto, G. A., Miche, Y., and Lendasse, A. (2015). Minimal learning machine: A novel supervised distance-based approach for regression and classification. Neurocomputing, 164:34 - 44.
Publicado
02/07/2017
Como Citar
PEREIRA, Tamara Arruda; DE SOUZA JUNIOR, Amauri Holanda.
Uma formulação para a máquina de aprendizagem mínima baseada em programação linear. In: SEMINÁRIO INTEGRADO DE SOFTWARE E HARDWARE (SEMISH), 44. , 2017, São Paulo.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2017
.
p. 2566-2574.
ISSN 2595-6205.
DOI: https://doi.org/10.5753/semish.2017.3364.
