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		<identifier>8JMKD3MGPBW34M/3EEQQUB</identifier>
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		<citationkey>EscalanteTaubNonaGold:2013:UsUnLe</citationkey>
		<author>Escalante, Diego Alonso Chávez,</author>
		<author>Taubin, Gabriel,</author>
		<author>Nonato, Luis Gustavo,</author>
		<author>Goldenstein, Siome Klein,</author>
		<affiliation>IC-UNICAMP</affiliation>
		<affiliation>School of Engineering, Brown University</affiliation>
		<affiliation>ICMC-USP</affiliation>
		<affiliation>IC-UNICAMP</affiliation>
		<title>Using Unsupervised Learning for Graph Construction in Semi-Supervised Learning with Graphs</title>
		<conferencename>Conference on Graphics, Patterns and Images, 26 (SIBGRAPI)</conferencename>
		<year>2013</year>
		<editor>Boyer, Kim,</editor>
		<editor>Hirata, Nina,</editor>
		<editor>Nedel, Luciana,</editor>
		<editor>Silva, Claudio,</editor>
		<booktitle>Proceedings</booktitle>
		<date>Aug. 5-8, 2013</date>
		<publisheraddress>Los Alamitos</publisheraddress>
		<publisher>IEEE Computer Society</publisher>
		<conferencelocation>Arequipa, Peru</conferencelocation>
		<keywords>Semi-Supervised Learning, Growing Neural Gas.</keywords>
		<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.</abstract>
		<language>en</language>
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