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		<doi>10.1109/SIBGRAPI.2009.20</doi>
		<citationkey>LageCaPeBoTaLeLo:2009:SuVeLe</citationkey>
		<title>Support Vectors Learning for Vector Field Reconstruction</title>
		<format>Printed, On-line.</format>
		<year>2009</year>
		<numberoffiles>1</numberoffiles>
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		<author>Lage, Marcos,</author>
		<author>Castro, Rener,</author>
		<author>Petronetto, Fabiano,</author>
		<author>Bordignon, Alex,</author>
		<author>Tavares, Geovan,</author>
		<author>Lewiner, Thomas,</author>
		<author>Lopes, Hélio,</author>
		<affiliation>Matmídia Laboratory – Department of Mathematics, PUC–Rio – Rio de Janeiro, Brazil</affiliation>
		<affiliation>.</affiliation>
		<affiliation>Matmídia Laboratory – Department of Mathematics, PUC–Rio – Rio de Janeiro, Brazil</affiliation>
		<affiliation>Matmídia Laboratory – Department of Mathematics, PUC–Rio – Rio de Janeiro, Brazil</affiliation>
		<affiliation>Matmídia Laboratory – Department of Mathematics, PUC–Rio – Rio de Janeiro, Brazil</affiliation>
		<affiliation>Matmídia Laboratory – Department of Mathematics, PUC–Rio – Rio de Janeiro, Brazil</affiliation>
		<affiliation>Matmídia Laboratory – Department of Mathematics, PUC–Rio – Rio de Janeiro, Brazil</affiliation>
		<editor>Nonato, Luis Gustavo,</editor>
		<editor>Scharcanski, Jacob,</editor>
		<e-mailaddress>lewiner@gmail.com</e-mailaddress>
		<conferencename>Brazilian Symposium on Computer Graphics and Image Processing, 22 (SIBGRAPI)</conferencename>
		<conferencelocation>Rio de Janeiro, RJ, Brazil</conferencelocation>
		<date>11-14 Oct. 2009</date>
		<publisher>IEEE Computer Society</publisher>
		<publisheraddress>Los Alamitos</publisheraddress>
		<booktitle>Proceedings</booktitle>
		<tertiarytype>Full Paper</tertiarytype>
		<transferableflag>1</transferableflag>
		<versiontype>finaldraft</versiontype>
		<keywords>Vector Field, Support Vector Machine.</keywords>
		<abstract>Sampled vector &#64257;elds generally appear as measurements of real phenomena. They can be obtained by the use of a Particle Image Velocimetry acquisition device, or as the result of a physical simulation, such as a &#64258;uid &#64258;ow simulation, among many examples. This paper proposes to formulate the unstructured vector &#64257;eld reconstruction and approximation through Machine-Learning. The machine learns from the samples a global vector &#64257;eld estimation function that could be evaluated at arbitrary points from the whole domain. Using an adaptation of the Support Vector Regression method for multi-scale analysis, the proposed method provides a global, analytical expression for the reconstructed vector &#64257;eld through an ef&#64257;cient non-linear optimization. Experiments on arti&#64257;cial and real data show a statistically robust behavior of the proposed technique.</abstract>
		<language>en</language>
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		<usergroup>lewiner@gmail.com</usergroup>
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