author = "Rocha, Anderson and Papa, Jo{\~a}o Paulo and Meira, Luis A. A.",
          affiliation = "Institute of Computing, University of Campinas (UNICAMP), Brazil 
                         and Department of Computer Science, State University of S{\~a}o 
                         Paulo (UNESP), Brazil and Department of Science and Technology, 
                         Federal University of S{\~a}o Paulo (UNIFESP), Brazil",
                title = "How Far You Can Get Using Machine Learning Black-Boxes",
            booktitle = "Proceedings...",
                 year = "2010",
               editor = "Bellon, Olga and Esperan{\c{c}}a, Claudio",
         organization = "Conference on Graphics, Patterns and Images, 23. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "Learning Black-Boxes, Metrics Space, Pattern Analysis, Support 
                         Vector Machines, Optimum-Path Forest, Neural Networks, K-Nearest 
             abstract = "Supervised Learning (SL) is a machine learning research area which 
                         aims at developing techniques able to take advantage from labeled 
                         training samples to make decisions over unseen examples. Recently, 
                         a lot of tools have been presented in order to perform machine 
                         learning in a more straightfor- ward and transparent manner. 
                         However, one problem that is increasingly present in most of the 
                         SL problems being solved is that, sometimes, researchers do not 
                         completely understand what supervised learning is and, more often 
                         than not, publish results using machine learning black-boxes. In 
                         this paper, we shed light over the use of machine learning 
                         black-boxes and show researchers how far they can get using these 
                         out-of-the- box solutions instead of going deeper into the 
                         machinery of the classifiers. Here, we focus on one aspect of 
                         classifiers namely the way they compare examples in the feature 
                         space and show how a simple knowledge about the classifiers 
                         machinery can lift the results way beyond out-of-the-box machine 
                         learning solutions.",
  conference-location = "Gramado",
      conference-year = "Aug. 30 - Sep. 3, 2010",
             language = "en",
           targetfile = "rocha-et-al-sibgrapi-2010-camera-ready.pdf",
        urlaccessdate = "2020, Nov. 29"