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		<citationkey>BenatoPapaMara:2016:AjFiPa</citationkey>
		<title>Ajuste fino de parâmetros de Redes Neurais por Convolução utilizando o Algoritmo de Otimização das Aves Migratórias</title>
		<format>On-line</format>
		<year>2016</year>
		<numberoffiles>1</numberoffiles>
		<size>178 KiB</size>
		<author>Benato, Bárbara Caroline,</author>
		<author>Papa, João Paulo,</author>
		<author>Marana, Aparecido Nilceu,</author>
		<affiliation>Sao Paulo State University</affiliation>
		<affiliation>Sao Paulo State University</affiliation>
		<affiliation>Sao Paulo State University</affiliation>
		<editor>Aliaga, Daniel G.,</editor>
		<editor>Davis, Larry S.,</editor>
		<editor>Farias, Ricardo C.,</editor>
		<editor>Fernandes, Leandro A. F.,</editor>
		<editor>Gibson, Stuart J.,</editor>
		<editor>Giraldi, Gilson A.,</editor>
		<editor>Gois, João Paulo,</editor>
		<editor>Maciel, Anderson,</editor>
		<editor>Menotti, David,</editor>
		<editor>Miranda, Paulo A. V.,</editor>
		<editor>Musse, Soraia,</editor>
		<editor>Namikawa, Laercio,</editor>
		<editor>Pamplona, Mauricio,</editor>
		<editor>Papa, João Paulo,</editor>
		<editor>Santos, Jefersson dos,</editor>
		<editor>Schwartz, William Robson,</editor>
		<editor>Thomaz, Carlos E.,</editor>
		<e-mailaddress>barbarabenato@gmail.com</e-mailaddress>
		<conferencename>Conference on Graphics, Patterns and Images, 29 (SIBGRAPI)</conferencename>
		<conferencelocation>São José dos Campos, SP, Brazil</conferencelocation>
		<date>4-7 Oct. 2016</date>
		<publisher>Sociedade Brasileira de Computação</publisher>
		<publisheraddress>Porto Alegre</publisheraddress>
		<booktitle>Proceedings</booktitle>
		<tertiarytype>Undergraduate Work</tertiarytype>
		<transferableflag>1</transferableflag>
		<keywords>aprendizado em profundidade, otimização meta-heurística.</keywords>
		<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.</abstract>
		<language>pt</language>
		<targetfile>paperBarbara_final.pdf</targetfile>
		<usergroup>barbarabenato@gmail.com</usergroup>
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