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  • Source: Neurocomputing. Unidade: ICMC

    Subjects: INTELIGÊNCIA ARTIFICIAL, APRENDIZADO COMPUTACIONAL

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      BERTON, Lilian et al. RGCLI: robust graph that considers labeled instances for semi-supervised learning. Neurocomputing, v. 226, p. 238-248, 2017Tradução . . Disponível em: https://doi.org/10.1016/j.neucom.2016.11.053. Acesso em: 04 out. 2024.
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      Berton, L., Faleiros, T. de P., Valejo, A., Valverde-Rebaza, J., & Lopes, A. de A. (2017). RGCLI: robust graph that considers labeled instances for semi-supervised learning. Neurocomputing, 226, 238-248. doi:10.1016/j.neucom.2016.11.053
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      Berton L, Faleiros T de P, Valejo A, Valverde-Rebaza J, Lopes A de A. RGCLI: robust graph that considers labeled instances for semi-supervised learning [Internet]. Neurocomputing. 2017 ; 226 238-248.[citado 2024 out. 04 ] Available from: https://doi.org/10.1016/j.neucom.2016.11.053
    • Vancouver

      Berton L, Faleiros T de P, Valejo A, Valverde-Rebaza J, Lopes A de A. RGCLI: robust graph that considers labeled instances for semi-supervised learning [Internet]. Neurocomputing. 2017 ; 226 238-248.[citado 2024 out. 04 ] Available from: https://doi.org/10.1016/j.neucom.2016.11.053
  • Source: Natural Computing. Unidade: ICMC

    Subjects: INTELIGÊNCIA ARTIFICIAL, COMPUTAÇÃO EVOLUTIVA, ALGORITMOS GENÉTICOS

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      HORVÁTH, Tomás e CARVALHO, André Carlos Ponce de Leon Ferreira de. Evolutionary computing in recommender systems: a review of recent research. Natural Computing, v. 16, n. 3, p. Se 2017, 2017Tradução . . Disponível em: https://doi.org/10.1007/s11047-016-9540-y. Acesso em: 04 out. 2024.
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      Horváth, T., & Carvalho, A. C. P. de L. F. de. (2017). Evolutionary computing in recommender systems: a review of recent research. Natural Computing, 16( 3), Se 2017. doi:10.1007/s11047-016-9540-y
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      Horváth T, Carvalho ACP de LF de. Evolutionary computing in recommender systems: a review of recent research [Internet]. Natural Computing. 2017 ; 16( 3): Se 2017.[citado 2024 out. 04 ] Available from: https://doi.org/10.1007/s11047-016-9540-y
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      Horváth T, Carvalho ACP de LF de. Evolutionary computing in recommender systems: a review of recent research [Internet]. Natural Computing. 2017 ; 16( 3): Se 2017.[citado 2024 out. 04 ] Available from: https://doi.org/10.1007/s11047-016-9540-y
  • Source: Artificial Intelligence Review. Unidade: ICMC

    Subjects: INTELIGÊNCIA ARTIFICIAL, RECONHECIMENTO DE PADRÕES

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      FARIA, Elaine R et al. Novelty detection in data stream. Artificial Intelligence Review, v. 45, n. 2, p. 235-269, 2016Tradução . . Disponível em: https://doi.org/10.1007/s10462-015-9444-8. Acesso em: 04 out. 2024.
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      Faria, E. R., Gonçalves, I. J. C. R., Carvalho, A. C. P. de L. F. de, & Gama, J. (2016). Novelty detection in data stream. Artificial Intelligence Review, 45( 2), 235-269. doi:10.1007/s10462-015-9444-8
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      Faria ER, Gonçalves IJCR, Carvalho ACP de LF de, Gama J. Novelty detection in data stream [Internet]. Artificial Intelligence Review. 2016 ; 45( 2): 235-269.[citado 2024 out. 04 ] Available from: https://doi.org/10.1007/s10462-015-9444-8
    • Vancouver

      Faria ER, Gonçalves IJCR, Carvalho ACP de LF de, Gama J. Novelty detection in data stream [Internet]. Artificial Intelligence Review. 2016 ; 45( 2): 235-269.[citado 2024 out. 04 ] Available from: https://doi.org/10.1007/s10462-015-9444-8
  • Source: Neurocomputing. Unidade: ICMC

    Subjects: INTELIGÊNCIA ARTIFICIAL, APRENDIZADO COMPUTACIONAL

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      GARCIA, Luís P. F e CARVALHO, André Carlos Ponce de Leon Ferreira de e LORENA, Ana C. Noise detection in the meta-learning level. Neurocomputing, v. 176, p. 14-25, 2016Tradução . . Disponível em: https://doi.org/10.1016/j.neucom.2014.12.100. Acesso em: 04 out. 2024.
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      Garcia, L. P. F., Carvalho, A. C. P. de L. F. de, & Lorena, A. C. (2016). Noise detection in the meta-learning level. Neurocomputing, 176, 14-25. doi:10.1016/j.neucom.2014.12.100
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      Garcia LPF, Carvalho ACP de LF de, Lorena AC. Noise detection in the meta-learning level [Internet]. Neurocomputing. 2016 ; 176 14-25.[citado 2024 out. 04 ] Available from: https://doi.org/10.1016/j.neucom.2014.12.100
    • Vancouver

      Garcia LPF, Carvalho ACP de LF de, Lorena AC. Noise detection in the meta-learning level [Internet]. Neurocomputing. 2016 ; 176 14-25.[citado 2024 out. 04 ] Available from: https://doi.org/10.1016/j.neucom.2014.12.100
  • Source: Neurocomputing. Unidade: ICMC

    Subjects: COMPUTAÇÃO GRÁFICA, PROCESSAMENTO DE IMAGENS, INTELIGÊNCIA ARTIFICIAL

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      PONTI, Moacir Antonelli e NAZARÉ, Tiago Santana de e THUMÉ, Gabriela S. Image quantization as a dimensionality reduction procedure in color and texture feature extraction. Neurocomputing, v. 173, n. Ja 2016, p. 385-396, 2016Tradução . . Disponível em: https://doi.org/10.1016/j.neucom.2015.04.114. Acesso em: 04 out. 2024.
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      Ponti, M. A., Nazaré, T. S. de, & Thumé, G. S. (2016). Image quantization as a dimensionality reduction procedure in color and texture feature extraction. Neurocomputing, 173( Ja 2016), 385-396. doi:10.1016/j.neucom.2015.04.114
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      Ponti MA, Nazaré TS de, Thumé GS. Image quantization as a dimensionality reduction procedure in color and texture feature extraction [Internet]. Neurocomputing. 2016 ; 173( Ja 2016): 385-396.[citado 2024 out. 04 ] Available from: https://doi.org/10.1016/j.neucom.2015.04.114
    • Vancouver

      Ponti MA, Nazaré TS de, Thumé GS. Image quantization as a dimensionality reduction procedure in color and texture feature extraction [Internet]. Neurocomputing. 2016 ; 173( Ja 2016): 385-396.[citado 2024 out. 04 ] Available from: https://doi.org/10.1016/j.neucom.2015.04.114
  • Source: Applied Intelligence. Unidade: ICMC

    Subjects: INTELIGÊNCIA ARTIFICIAL, REDES NEURAIS

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      ROSA, João Luís Garcia e PIAZENTIN, Denis R. M. A new cognitive filtering approach based on Freeman K3 Neural Networks. Applied Intelligence, v. 45, n. 2, p. Se 2016, 2016Tradução . . Disponível em: https://doi.org/10.1007/s10489-016-0772-4. Acesso em: 04 out. 2024.
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      Rosa, J. L. G., & Piazentin, D. R. M. (2016). A new cognitive filtering approach based on Freeman K3 Neural Networks. Applied Intelligence, 45( 2), Se 2016. doi:10.1007/s10489-016-0772-4
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      Rosa JLG, Piazentin DRM. A new cognitive filtering approach based on Freeman K3 Neural Networks [Internet]. Applied Intelligence. 2016 ; 45( 2): Se 2016.[citado 2024 out. 04 ] Available from: https://doi.org/10.1007/s10489-016-0772-4
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      Rosa JLG, Piazentin DRM. A new cognitive filtering approach based on Freeman K3 Neural Networks [Internet]. Applied Intelligence. 2016 ; 45( 2): Se 2016.[citado 2024 out. 04 ] Available from: https://doi.org/10.1007/s10489-016-0772-4
  • Source: Data Mining and Knowledge Discovery. Unidade: ICMC

    Assunto: INTELIGÊNCIA ARTIFICIAL

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      CAMPOS, Guilherme O et al. On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study. Data Mining and Knowledge Discovery, v. 30, n. 4, p. 891-927, 2016Tradução . . Disponível em: https://doi.org/10.1007/s10618-015-0444-8. Acesso em: 04 out. 2024.
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      Campos, G. O., Zimek, A., Sander, J., Campello, R. J. G. B., Micenková, B., Schubert, E., et al. (2016). On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study. Data Mining and Knowledge Discovery, 30( 4), 891-927. doi:10.1007/s10618-015-0444-8
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      Campos GO, Zimek A, Sander J, Campello RJGB, Micenková B, Schubert E, Assent I, Houle ME. On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study [Internet]. Data Mining and Knowledge Discovery. 2016 ; 30( 4): 891-927.[citado 2024 out. 04 ] Available from: https://doi.org/10.1007/s10618-015-0444-8
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      Campos GO, Zimek A, Sander J, Campello RJGB, Micenková B, Schubert E, Assent I, Houle ME. On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study [Internet]. Data Mining and Knowledge Discovery. 2016 ; 30( 4): 891-927.[citado 2024 out. 04 ] Available from: https://doi.org/10.1007/s10618-015-0444-8
  • Source: Information Retrieval Journal. Unidade: ICMC

    Subjects: INTELIGÊNCIA ARTIFICIAL, APRENDIZADO COMPUTACIONAL, MINERAÇÃO DE DADOS, RECONHECIMENTO DE TEXTO, WORLD WIDE WEB

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      MANZATO, Marcelo Garcia et al. Mining unstructured content for recommender systems: an ensemble approach. Information Retrieval Journal, v. 19, n. 4, p. 378-415, 2016Tradução . . Disponível em: https://doi.org/10.1007/s10791-016-9280-8. Acesso em: 04 out. 2024.
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      Manzato, M. G., Domingues, M. A., Fortes, A. C., Sundermann, C. V., D'Addio, R. M., Conrado, M. S., et al. (2016). Mining unstructured content for recommender systems: an ensemble approach. Information Retrieval Journal, 19( 4), 378-415. doi:10.1007/s10791-016-9280-8
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      Manzato MG, Domingues MA, Fortes AC, Sundermann CV, D'Addio RM, Conrado MS, Rezende SO, Pimentel M da GC. Mining unstructured content for recommender systems: an ensemble approach [Internet]. Information Retrieval Journal. 2016 ; 19( 4): 378-415.[citado 2024 out. 04 ] Available from: https://doi.org/10.1007/s10791-016-9280-8
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      Manzato MG, Domingues MA, Fortes AC, Sundermann CV, D'Addio RM, Conrado MS, Rezende SO, Pimentel M da GC. Mining unstructured content for recommender systems: an ensemble approach [Internet]. Information Retrieval Journal. 2016 ; 19( 4): 378-415.[citado 2024 out. 04 ] Available from: https://doi.org/10.1007/s10791-016-9280-8
  • Source: Journal of Digital Imaging. Unidades: FMRP, ICMC

    Subjects: INTELIGÊNCIA ARTIFICIAL, BANCO DE DADOS, PROCESSAMENTO DE IMAGENS, RECONHECIMENTO DE IMAGEM

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      BÊDO, Marcos Vinícius Naves et al. Endowing a content-based medical image retrieval system with perceptual similarity using ensemble strategy. Journal of Digital Imaging, v. 29, n. 1, p. 22-37, 2016Tradução . . Disponível em: https://doi.org/10.1007/s10278-015-9809-1. Acesso em: 04 out. 2024.
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      Bêdo, M. V. N., Santos, D. P. dos, Ponciano-Silva, M., Marques, P. M. de A., Carvalho, A. C. P. de L. F. de, & Traina Junior, C. (2016). Endowing a content-based medical image retrieval system with perceptual similarity using ensemble strategy. Journal of Digital Imaging, 29( 1), 22-37. doi:10.1007/s10278-015-9809-1
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      Bêdo MVN, Santos DP dos, Ponciano-Silva M, Marques PM de A, Carvalho ACP de LF de, Traina Junior C. Endowing a content-based medical image retrieval system with perceptual similarity using ensemble strategy [Internet]. Journal of Digital Imaging. 2016 ; 29( 1): 22-37.[citado 2024 out. 04 ] Available from: https://doi.org/10.1007/s10278-015-9809-1
    • Vancouver

      Bêdo MVN, Santos DP dos, Ponciano-Silva M, Marques PM de A, Carvalho ACP de LF de, Traina Junior C. Endowing a content-based medical image retrieval system with perceptual similarity using ensemble strategy [Internet]. Journal of Digital Imaging. 2016 ; 29( 1): 22-37.[citado 2024 out. 04 ] Available from: https://doi.org/10.1007/s10278-015-9809-1
  • Source: Neurocomputing. Unidades: FFCLRP, ICMC

    Subjects: INTELIGÊNCIA ARTIFICIAL, RECONHECIMENTO DE OBJETOS

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      BENICASA, Alcides X et al. An object-based visual selection framework. Neurocomputing, v. 180, p. 35-54, 2016Tradução . . Disponível em: https://doi.org/10.1016/j.neucom.2015.10.111. Acesso em: 04 out. 2024.
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      Benicasa, A. X., Quiles, M. G., Silva, T. C., Liang, Z., & Romero, R. A. F. (2016). An object-based visual selection framework. Neurocomputing, 180, 35-54. doi:10.1016/j.neucom.2015.10.111
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      Benicasa AX, Quiles MG, Silva TC, Liang Z, Romero RAF. An object-based visual selection framework [Internet]. Neurocomputing. 2016 ; 180 35-54.[citado 2024 out. 04 ] Available from: https://doi.org/10.1016/j.neucom.2015.10.111
    • Vancouver

      Benicasa AX, Quiles MG, Silva TC, Liang Z, Romero RAF. An object-based visual selection framework [Internet]. Neurocomputing. 2016 ; 180 35-54.[citado 2024 out. 04 ] Available from: https://doi.org/10.1016/j.neucom.2015.10.111
  • Source: Neurocomputing. Unidade: ICMC

    Subjects: INTELIGÊNCIA ARTIFICIAL, APRENDIZADO COMPUTACIONAL

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      SPOLAÔR, Newton et al. A systematic review of multi-label feature selection and a new method based on label construction. Neurocomputing, v. 180, p. 3-15, 2016Tradução . . Disponível em: https://doi.org/10.1016/j.neucom.2015.07.118. Acesso em: 04 out. 2024.
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      Spolaôr, N., Monard, M. C., Tsoumakas, G., & Lee, H. D. (2016). A systematic review of multi-label feature selection and a new method based on label construction. Neurocomputing, 180, 3-15. doi:10.1016/j.neucom.2015.07.118
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      Spolaôr N, Monard MC, Tsoumakas G, Lee HD. A systematic review of multi-label feature selection and a new method based on label construction [Internet]. Neurocomputing. 2016 ; 180 3-15.[citado 2024 out. 04 ] Available from: https://doi.org/10.1016/j.neucom.2015.07.118
    • Vancouver

      Spolaôr N, Monard MC, Tsoumakas G, Lee HD. A systematic review of multi-label feature selection and a new method based on label construction [Internet]. Neurocomputing. 2016 ; 180 3-15.[citado 2024 out. 04 ] Available from: https://doi.org/10.1016/j.neucom.2015.07.118
  • Source: Information Sciences. Unidade: ICMC

    Subjects: INTELIGÊNCIA ARTIFICIAL, APRENDIZADO COMPUTACIONAL, MÍDIAS SOCIAIS

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      SILVA, Nádia Felix Felipe da et al. Using unsupervised information to improve semi-supervised tweet sentiment classification. Information Sciences, v. 355, p. 348-365, 2016Tradução . . Disponível em: https://doi.org/10.1016/j.ins.2016.02.002. Acesso em: 04 out. 2024.
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      Silva, N. F. F. da, Coletta, L. F. S., Hruschka, E. R., & Hruschka Junior, E. R. (2016). Using unsupervised information to improve semi-supervised tweet sentiment classification. Information Sciences, 355, 348-365. doi:10.1016/j.ins.2016.02.002
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      Silva NFF da, Coletta LFS, Hruschka ER, Hruschka Junior ER. Using unsupervised information to improve semi-supervised tweet sentiment classification [Internet]. Information Sciences. 2016 ; 355 348-365.[citado 2024 out. 04 ] Available from: https://doi.org/10.1016/j.ins.2016.02.002
    • Vancouver

      Silva NFF da, Coletta LFS, Hruschka ER, Hruschka Junior ER. Using unsupervised information to improve semi-supervised tweet sentiment classification [Internet]. Information Sciences. 2016 ; 355 348-365.[citado 2024 out. 04 ] Available from: https://doi.org/10.1016/j.ins.2016.02.002
  • Source: Neurocomputing. Unidade: ICMC

    Assunto: INTELIGÊNCIA ARTIFICIAL

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      GARCIA, Luís P. F e CARVALHO, André Carlos Ponce de Leon Ferreira de e LORENA, Ana C. Effect of label noise in the complexity of classification problems. Neurocomputing, v. 160, p. 108-119, 2015Tradução . . Disponível em: https://doi.org/10.1016/j.neucom.2014.10.085. Acesso em: 04 out. 2024.
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      Garcia, L. P. F., Carvalho, A. C. P. de L. F. de, & Lorena, A. C. (2015). Effect of label noise in the complexity of classification problems. Neurocomputing, 160, 108-119. doi:10.1016/j.neucom.2014.10.085
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      Garcia LPF, Carvalho ACP de LF de, Lorena AC. Effect of label noise in the complexity of classification problems [Internet]. Neurocomputing. 2015 ; 160 108-119.[citado 2024 out. 04 ] Available from: https://doi.org/10.1016/j.neucom.2014.10.085
    • Vancouver

      Garcia LPF, Carvalho ACP de LF de, Lorena AC. Effect of label noise in the complexity of classification problems [Internet]. Neurocomputing. 2015 ; 160 108-119.[citado 2024 out. 04 ] Available from: https://doi.org/10.1016/j.neucom.2014.10.085
  • Source: Knowledge and Information Systems. Unidade: ICMC

    Subjects: INTELIGÊNCIA ARTIFICIAL, APRENDIZADO COMPUTACIONAL, MINERAÇÃO DE DADOS, RECONHECIMENTO DE PADRÕES

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      PRATI, Ronaldo C e BATISTA, Gustavo Enrique de Almeida Prado Alves e SILVA, Diego Furtado. Class imbalance revisited: a new experimental setup to assess the performance of treatment methods. Knowledge and Information Systems, v. 45, n. 1, p. 247-270, 2015Tradução . . Disponível em: https://doi.org/10.1007/s10115-014-0794-3. Acesso em: 04 out. 2024.
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      Prati, R. C., Batista, G. E. de A. P. A., & Silva, D. F. (2015). Class imbalance revisited: a new experimental setup to assess the performance of treatment methods. Knowledge and Information Systems, 45( 1), 247-270. doi:10.1007/s10115-014-0794-3
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      Prati RC, Batista GE de APA, Silva DF. Class imbalance revisited: a new experimental setup to assess the performance of treatment methods [Internet]. Knowledge and Information Systems. 2015 ; 45( 1): 247-270.[citado 2024 out. 04 ] Available from: https://doi.org/10.1007/s10115-014-0794-3
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      Prati RC, Batista GE de APA, Silva DF. Class imbalance revisited: a new experimental setup to assess the performance of treatment methods [Internet]. Knowledge and Information Systems. 2015 ; 45( 1): 247-270.[citado 2024 out. 04 ] Available from: https://doi.org/10.1007/s10115-014-0794-3
  • Source: Knowledge-Based Systems. Unidade: ICMC

    Subjects: INTELIGÊNCIA ARTIFICIAL, APRENDIZADO COMPUTACIONAL, MINERAÇÃO DE DADOS

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      PEREIRA, Andre Luiz Vizine e HRUSCHKA, Eduardo Raul. Simultaneous co-clustering and learning to address the cold start problem in recommender systems. Knowledge-Based Systems, v. 82, p. 11-19, 2015Tradução . . Disponível em: https://doi.org/10.1016/j.knosys.2015.02.016. Acesso em: 04 out. 2024.
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      Pereira, A. L. V., & Hruschka, E. R. (2015). Simultaneous co-clustering and learning to address the cold start problem in recommender systems. Knowledge-Based Systems, 82, 11-19. doi:10.1016/j.knosys.2015.02.016
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      Pereira ALV, Hruschka ER. Simultaneous co-clustering and learning to address the cold start problem in recommender systems [Internet]. Knowledge-Based Systems. 2015 ; 82 11-19.[citado 2024 out. 04 ] Available from: https://doi.org/10.1016/j.knosys.2015.02.016
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      Pereira ALV, Hruschka ER. Simultaneous co-clustering and learning to address the cold start problem in recommender systems [Internet]. Knowledge-Based Systems. 2015 ; 82 11-19.[citado 2024 out. 04 ] Available from: https://doi.org/10.1016/j.knosys.2015.02.016
  • Source: Genetic Programming and Evolvable Machines. Unidade: ICMC

    Subjects: INTELIGÊNCIA ARTIFICIAL, COMPUTAÇÃO EVOLUTIVA, ALGORITMOS GENÉTICOS

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      BARROS, Rodrigo C e BASGALUPP, Márcio P e CARVALHO, André Carlos Ponce de Leon Ferreira de. Investigating fitness functions for a hyper-heuristic evolutionary algorithm in the context of balanced and imbalanced data classification. Genetic Programming and Evolvable Machines, v. 16, n. 3, p. Se 2015, 2015Tradução . . Disponível em: https://doi.org/10.1007/s10710-014-9235-z. Acesso em: 04 out. 2024.
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      Barros, R. C., Basgalupp, M. P., & Carvalho, A. C. P. de L. F. de. (2015). Investigating fitness functions for a hyper-heuristic evolutionary algorithm in the context of balanced and imbalanced data classification. Genetic Programming and Evolvable Machines, 16( 3), Se 2015. doi:10.1007/s10710-014-9235-z
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      Barros RC, Basgalupp MP, Carvalho ACP de LF de. Investigating fitness functions for a hyper-heuristic evolutionary algorithm in the context of balanced and imbalanced data classification [Internet]. Genetic Programming and Evolvable Machines. 2015 ; 16( 3): Se 2015.[citado 2024 out. 04 ] Available from: https://doi.org/10.1007/s10710-014-9235-z
    • Vancouver

      Barros RC, Basgalupp MP, Carvalho ACP de LF de. Investigating fitness functions for a hyper-heuristic evolutionary algorithm in the context of balanced and imbalanced data classification [Internet]. Genetic Programming and Evolvable Machines. 2015 ; 16( 3): Se 2015.[citado 2024 out. 04 ] Available from: https://doi.org/10.1007/s10710-014-9235-z
  • Source: Journal of the Brazilian Computer Society. Unidade: ICMC

    Subjects: INTELIGÊNCIA ARTIFICIAL, PROCESSAMENTO DE LINGUAGEM NATURAL, REDES NEURAIS

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      FONSECA, Erick R e ROSA, João Luís Garcia e ALUÍSIO, Sandra Maria. Evaluating word embeddings and a revised corpus for part-of-speech tagging in Portuguese. Journal of the Brazilian Computer Society, v. 21, p. 1-14, 2015Tradução . . Disponível em: https://doi.org/10.1186/s13173-014-0020-x. Acesso em: 04 out. 2024.
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      Fonseca, E. R., Rosa, J. L. G., & Aluísio, S. M. (2015). Evaluating word embeddings and a revised corpus for part-of-speech tagging in Portuguese. Journal of the Brazilian Computer Society, 21, 1-14. doi:10.1186/s13173-014-0020-x
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      Fonseca ER, Rosa JLG, Aluísio SM. Evaluating word embeddings and a revised corpus for part-of-speech tagging in Portuguese [Internet]. Journal of the Brazilian Computer Society. 2015 ; 21 1-14.[citado 2024 out. 04 ] Available from: https://doi.org/10.1186/s13173-014-0020-x
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      Fonseca ER, Rosa JLG, Aluísio SM. Evaluating word embeddings and a revised corpus for part-of-speech tagging in Portuguese [Internet]. Journal of the Brazilian Computer Society. 2015 ; 21 1-14.[citado 2024 out. 04 ] Available from: https://doi.org/10.1186/s13173-014-0020-x
  • Source: Pattern Recognition Letters. Unidade: ICMC

    Subjects: INTELIGÊNCIA ARTIFICIAL, MINERAÇÃO DE DADOS

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      CORRÊA, Geraldo N et al. Interactive textual feature selection for consensus clustering. Pattern Recognition Letters, v. 52, n. ja 2015, p. 25-31, 2015Tradução . . Disponível em: https://doi.org/10.1016/j.patrec.2014.09.008. Acesso em: 04 out. 2024.
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      Corrêa, G. N., Marcacini, R. M., Hruschka, E. R., & Rezende, S. O. (2015). Interactive textual feature selection for consensus clustering. Pattern Recognition Letters, 52( ja 2015), 25-31. doi:10.1016/j.patrec.2014.09.008
    • NLM

      Corrêa GN, Marcacini RM, Hruschka ER, Rezende SO. Interactive textual feature selection for consensus clustering [Internet]. Pattern Recognition Letters. 2015 ; 52( ja 2015): 25-31.[citado 2024 out. 04 ] Available from: https://doi.org/10.1016/j.patrec.2014.09.008
    • Vancouver

      Corrêa GN, Marcacini RM, Hruschka ER, Rezende SO. Interactive textual feature selection for consensus clustering [Internet]. Pattern Recognition Letters. 2015 ; 52( ja 2015): 25-31.[citado 2024 out. 04 ] Available from: https://doi.org/10.1016/j.patrec.2014.09.008
  • Source: IEEE Transactions on Visualization and Computer Graphics. Unidade: ICMC

    Subjects: COMPUTAÇÃO GRÁFICA, PROCESSAMENTO DE IMAGENS, INTELIGÊNCIA ARTIFICIAL

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      PAIVA, José Gustavo S et al. An approach to supporting incremental visual data classification. IEEE Transactions on Visualization and Computer Graphics, v. 21, n. ja 2015, p. 4-17, 2015Tradução . . Disponível em: https://doi.org/10.1109/TVCG.2014.2331979. Acesso em: 04 out. 2024.
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      Paiva, J. G. S., Schwartz, W. R., Pedrini, H., & Minghim, R. (2015). An approach to supporting incremental visual data classification. IEEE Transactions on Visualization and Computer Graphics, 21( ja 2015), 4-17. doi:10.1109/TVCG.2014.2331979
    • NLM

      Paiva JGS, Schwartz WR, Pedrini H, Minghim R. An approach to supporting incremental visual data classification [Internet]. IEEE Transactions on Visualization and Computer Graphics. 2015 ; 21( ja 2015): 4-17.[citado 2024 out. 04 ] Available from: https://doi.org/10.1109/TVCG.2014.2331979
    • Vancouver

      Paiva JGS, Schwartz WR, Pedrini H, Minghim R. An approach to supporting incremental visual data classification [Internet]. IEEE Transactions on Visualization and Computer Graphics. 2015 ; 21( ja 2015): 4-17.[citado 2024 out. 04 ] Available from: https://doi.org/10.1109/TVCG.2014.2331979
  • Source: IEEE Transactions on Visualization and Computer Graphics. Unidade: ICMC

    Subjects: COMPUTAÇÃO GRÁFICA, PROCESSAMENTO DE IMAGENS, INTELIGÊNCIA ARTIFICIAL

    Acesso à fonteDOIHow to cite
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    • ABNT

      ETEMADPOUR, Ronak et al. Perception-based evaluation of projection methods for multidimensional data visualization. IEEE Transactions on Visualization and Computer Graphics, v. 21, n. ja 2015, p. 81-94, 2015Tradução . . Disponível em: https://doi.org/10.1109/TVCG.2014.2330617. Acesso em: 04 out. 2024.
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      Etemadpour, R., Motta, R., Paiva, J. G. de S., Minghim, R., Oliveira, M. C. F. de, & Linsen, L. (2015). Perception-based evaluation of projection methods for multidimensional data visualization. IEEE Transactions on Visualization and Computer Graphics, 21( ja 2015), 81-94. doi:10.1109/TVCG.2014.2330617
    • NLM

      Etemadpour R, Motta R, Paiva JG de S, Minghim R, Oliveira MCF de, Linsen L. Perception-based evaluation of projection methods for multidimensional data visualization [Internet]. IEEE Transactions on Visualization and Computer Graphics. 2015 ; 21( ja 2015): 81-94.[citado 2024 out. 04 ] Available from: https://doi.org/10.1109/TVCG.2014.2330617
    • Vancouver

      Etemadpour R, Motta R, Paiva JG de S, Minghim R, Oliveira MCF de, Linsen L. Perception-based evaluation of projection methods for multidimensional data visualization [Internet]. IEEE Transactions on Visualization and Computer Graphics. 2015 ; 21( ja 2015): 81-94.[citado 2024 out. 04 ] Available from: https://doi.org/10.1109/TVCG.2014.2330617

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