@InProceedings{EscalanteTaubNonaGold:2013:UsUnLe,
               author = "Escalante, Diego Alonso Ch{\'a}vez and Taubin, Gabriel and 
                         Nonato, Luis Gustavo and Goldenstein, Siome Klein",
          affiliation = "IC-UNICAMP and School of Engineering, Brown University and 
                         ICMC-USP and IC-UNICAMP",
                title = "Using Unsupervised Learning for Graph Construction in 
                         Semi-Supervised Learning with Graphs",
            booktitle = "Proceedings...",
                 year = "2013",
               editor = "Boyer, Kim and Hirata, Nina and Nedel, Luciana and Silva, 
                         Claudio",
         organization = "Conference on Graphics, Patterns and Images, 26. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "Semi-Supervised Learning, Growing Neural Gas.",
             abstract = "Semi-supervised Learning with Graphs can achieve good results in 
                         classification tasks even in difficult conditions. Unfortunately, 
                         it can be slow and use a lot of memory. The first important step 
                         of the graph-based semi-supervised learning approaches is the 
                         construction of the graph from the data, where each data-point 
                         usually becomes a vertex in the graph a potential problem with 
                         large amounts of data. In this paper, we present a graph 
                         construction method that uses an unsupervised neural network 
                         called growing neural gas (GNG). The GNG instance presents a 
                         intelligent stopping criteria that determines when the final 
                         network configuration maps correctly the input- data points. With 
                         that in mind, we use the final trained network as a reduced input 
                         graph for the semi-supervised classification algorithm, 
                         associating original data-points to the neurons they have 
                         activated in the unsupervised training process.",
  conference-location = "Arequipa, Peru",
      conference-year = "5-8 Aug. 2013",
                  doi = "10.1109/SIBGRAPI.2013.13",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI.2013.13",
             language = "en",
                  ibi = "8JMKD3MGPBW34M/3EEQQUB",
                  url = "http://urlib.net/ibi/8JMKD3MGPBW34M/3EEQQUB",
           targetfile = "114517.pdf",
        urlaccessdate = "2024, Apr. 28"
}