@InProceedings{BenatoPapaMara:2016:AjFiPa,
               author = "Benato, B{\'a}rbara Caroline and Papa, Jo{\~a}o Paulo and 
                         Marana, Aparecido Nilceu",
          affiliation = "{Sao Paulo State University} and {Sao Paulo State University} and 
                         {Sao Paulo State University}",
                title = "Ajuste fino de par{\^a}metros de Redes Neurais por 
                         Convolu{\c{c}}{\~a}o utilizando o Algoritmo de 
                         Otimiza{\c{c}}{\~a}o das Aves Migrat{\'o}rias",
            booktitle = "Proceedings...",
                 year = "2016",
               editor = "Aliaga, Daniel G. and Davis, Larry S. and Farias, Ricardo C. and 
                         Fernandes, Leandro A. F. and Gibson, Stuart J. and Giraldi, Gilson 
                         A. and Gois, Jo{\~a}o Paulo and Maciel, Anderson and Menotti, 
                         David and Miranda, Paulo A. V. and Musse, Soraia and Namikawa, 
                         Laercio and Pamplona, Mauricio and Papa, Jo{\~a}o Paulo and 
                         Santos, Jefersson dos and Schwartz, William Robson and Thomaz, 
                         Carlos E.",
         organization = "Conference on Graphics, Patterns and Images, 29. (SIBGRAPI)",
            publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
              address = "Porto Alegre",
             keywords = "aprendizado em profundidade, otimiza{\c{c}}{\~a}o 
                         meta-heur{\'{\i}}stica.",
             abstract = "The problem of fine-tuning parameters in deep learning techniques 
                         has been considerably focused in the last years, since to 
                         hand-tune them is painful and prone to errors. In this work, we 
                         introduced the Migrating Birds Optimization (MBO) to fine-tune 
                         parameters of Convolutional Neural Networks (CNNs) and Deep Belief 
                         Networks (DBNs), being the results compared against two other 
                         state-of-the-art meta-heuristic techniques. The experiments showed 
                         MBO obtained very good results in both CNNs and DBNs, but at the 
                         price of a high computational burden.",
  conference-location = "S{\~a}o Jos{\'e} dos Campos, SP, Brazil",
      conference-year = "4-7 Oct. 2016",
             language = "pt",
                  ibi = "8JMKD3MGPAW/3MD5ASL",
                  url = "http://urlib.net/ibi/8JMKD3MGPAW/3MD5ASL",
           targetfile = "paperBarbara_final.pdf",
        urlaccessdate = "2024, Apr. 29"
}