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  • In: Ceramics International. Unidade: ICMC

    Subjects: Algoritmos Genéticos, Método Dos Elementos Finitos, Refratários, Fornos Metalúrgicos

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      SANTOS, Dulce P; PELISSARI, Pedro Ivo Batistel Galiote Brossi; OLIVEIRA, B. S. de; et al. Materials selection of furnace linings with multi-component refractory ceramics based on an evolutionary screening procedure. Ceramics International, Kidlington, Elsevier, v. 46, n. 4, p. 4113-4125, 2020. Disponível em: < https://doi.org/10.1016/j.ceramint.2019.10.127 > DOI: 10.1016/j.ceramint.2019.10.127.
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      Santos, D. P., Pelissari, P. I. B. G. B., Oliveira, B. S. de, Leiva, D. R., Mello, R. F. de, & Pandolfelli, V. C. (2020). Materials selection of furnace linings with multi-component refractory ceramics based on an evolutionary screening procedure. Ceramics International, 46( 4), 4113-4125. doi:10.1016/j.ceramint.2019.10.127
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      Santos DP, Pelissari PIBGB, Oliveira BS de, Leiva DR, Mello RF de, Pandolfelli VC. Materials selection of furnace linings with multi-component refractory ceramics based on an evolutionary screening procedure [Internet]. Ceramics International. 2020 ; 46( 4): 4113-4125.Available from: https://doi.org/10.1016/j.ceramint.2019.10.127
    • Vancouver

      Santos DP, Pelissari PIBGB, Oliveira BS de, Leiva DR, Mello RF de, Pandolfelli VC. Materials selection of furnace linings with multi-component refractory ceramics based on an evolutionary screening procedure [Internet]. Ceramics International. 2020 ; 46( 4): 4113-4125.Available from: https://doi.org/10.1016/j.ceramint.2019.10.127
  • In: Connection Science. Unidade: ICMC

    Subjects: Computação Em Nuvem, Recuperação Da Informação, Criptologia

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      XIAO, Tingting; HAN, Dezhi; HE, Junhui; LI, Kuan-Ching; MELLO, Rodrigo Fernandes de. Multi-keyword ranked search based on mapping set matching in cloud ciphertext storage system. Connection Science, Abingdon, Taylor & Francis, 2020. Disponível em: < https://doi.org/10.1080/09540091.2020.1753175 > DOI: 10.1080/09540091.2020.1753175.
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      Xiao, T., Han, D., He, J., Li, K. -C., & Mello, R. F. de. (2020). Multi-keyword ranked search based on mapping set matching in cloud ciphertext storage system. Connection Science. doi:10.1080/09540091.2020.1753175
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      Xiao T, Han D, He J, Li K-C, Mello RF de. Multi-keyword ranked search based on mapping set matching in cloud ciphertext storage system [Internet]. Connection Science. 2020 ;Available from: https://doi.org/10.1080/09540091.2020.1753175
    • Vancouver

      Xiao T, Han D, He J, Li K-C, Mello RF de. Multi-keyword ranked search based on mapping set matching in cloud ciphertext storage system [Internet]. Connection Science. 2020 ;Available from: https://doi.org/10.1080/09540091.2020.1753175
  • In: International Journal of Computational Science and Engineering. Unidade: ICMC

    Subjects: Aprendizado Computacional, Análise De Séries Temporais, Reconhecimento De Padrões

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      SILVA, Mirlei M. da; MELLO, Rodrigo Fernandes de; RIOS, Ricardo A. Time series clustering using stochastic and deterministic influences. International Journal of Computational Science and Engineering, Olney, Inderscience Publishers, v. 21, n. 3, p. 394-417, 2020. Disponível em: < https://doi.org/10.1504/IJCSE.2020.106063 > DOI: 10.1504/IJCSE.2020.106063.
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      Silva, M. M. da, Mello, R. F. de, & Rios, R. A. (2020). Time series clustering using stochastic and deterministic influences. International Journal of Computational Science and Engineering, 21( 3), 394-417. doi:10.1504/IJCSE.2020.106063
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      Silva MM da, Mello RF de, Rios RA. Time series clustering using stochastic and deterministic influences [Internet]. International Journal of Computational Science and Engineering. 2020 ; 21( 3): 394-417.Available from: https://doi.org/10.1504/IJCSE.2020.106063
    • Vancouver

      Silva MM da, Mello RF de, Rios RA. Time series clustering using stochastic and deterministic influences [Internet]. International Journal of Computational Science and Engineering. 2020 ; 21( 3): 394-417.Available from: https://doi.org/10.1504/IJCSE.2020.106063
  • In: Data in Brief. Unidade: ICMC

    Subjects: Análise De Séries Temporais, Redes Neurais, Vulcões

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      CANÁRIO, João Paulo; MELLO, Rodrigo Fernandes de; CURILEM, Millaray; HUENUPAN, Fernando; RIOS, Ricardo Araújo. Llaima volcano dataset: in-depth comparison of deep artificial neural network architectures on seismic events classification. Data in Brief, Amsterdam, Elsevier, v. 30, p. 1-6, 2020. Disponível em: < https://doi.org/10.1016/j.dib.2020.105627 > DOI: 10.1016/j.dib.2020.105627.
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      Canário, J. P., Mello, R. F. de, Curilem, M., Huenupan, F., & Rios, R. A. (2020). Llaima volcano dataset: in-depth comparison of deep artificial neural network architectures on seismic events classification. Data in Brief, 30, 1-6. doi:10.1016/j.dib.2020.105627
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      Canário JP, Mello RF de, Curilem M, Huenupan F, Rios RA. Llaima volcano dataset: in-depth comparison of deep artificial neural network architectures on seismic events classification [Internet]. Data in Brief. 2020 ; 30 1-6.Available from: https://doi.org/10.1016/j.dib.2020.105627
    • Vancouver

      Canário JP, Mello RF de, Curilem M, Huenupan F, Rios RA. Llaima volcano dataset: in-depth comparison of deep artificial neural network architectures on seismic events classification [Internet]. Data in Brief. 2020 ; 30 1-6.Available from: https://doi.org/10.1016/j.dib.2020.105627
  • In: Journal of Volcanology and Geothermal Research. Unidade: ICMC

    Subjects: Análise De Séries Temporais, Redes Neurais, Vulcões, Análise De Ondaletas

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      CANÁRIO, João Paulo; MELLO, Rodrigo Fernandes de; CURILEM, Millaray; HUENUPAN, Fernando; RIOS, Ricardo Araújo. In-depth comparison of deep artificial neural network architectures on seismic events classification. Journal of Volcanology and Geothermal Research, Amsterdam, Elsevier, 2020. Disponível em: < https://doi.org/10.1016/j.jvolgeores.2020.106881 > DOI: 10.1016/j.jvolgeores.2020.106881.
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      Canário, J. P., Mello, R. F. de, Curilem, M., Huenupan, F., & Rios, R. A. (2020). In-depth comparison of deep artificial neural network architectures on seismic events classification. Journal of Volcanology and Geothermal Research. doi:10.1016/j.jvolgeores.2020.106881
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      Canário JP, Mello RF de, Curilem M, Huenupan F, Rios RA. In-depth comparison of deep artificial neural network architectures on seismic events classification [Internet]. Journal of Volcanology and Geothermal Research. 2020 ;Available from: https://doi.org/10.1016/j.jvolgeores.2020.106881
    • Vancouver

      Canário JP, Mello RF de, Curilem M, Huenupan F, Rios RA. In-depth comparison of deep artificial neural network architectures on seismic events classification [Internet]. Journal of Volcanology and Geothermal Research. 2020 ;Available from: https://doi.org/10.1016/j.jvolgeores.2020.106881
  • In: Journal of Applied Physics. Unidade: ICMC

    Subjects: Aprendizado Computacional, Método Dos Elementos Finitos, Forno Elétrico, Transferência De Calor

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      SANTOS, Denise P; PELISSARI, Pedro Ivo Batistel Galiote Brossi; MELLO, Rodrigo Fernandes de; PANDOLFELLI, Victor Carlos. Estimating the thermal insulating performance of multi-component refractory ceramic systems based on a machine learning surrogate model framework. Journal of Applied Physics, Melville, AIP Publishing, v. 127, n. 21, p. 215104-1-215104-7, 2020. Disponível em: < https://doi.org/10.1063/5.0004395 > DOI: 10.1063/5.0004395.
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      Santos, D. P., Pelissari, P. I. B. G. B., Mello, R. F. de, & Pandolfelli, V. C. (2020). Estimating the thermal insulating performance of multi-component refractory ceramic systems based on a machine learning surrogate model framework. Journal of Applied Physics, 127( 21), 215104-1-215104-7. doi:10.1063/5.0004395
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      Santos DP, Pelissari PIBGB, Mello RF de, Pandolfelli VC. Estimating the thermal insulating performance of multi-component refractory ceramic systems based on a machine learning surrogate model framework [Internet]. Journal of Applied Physics. 2020 ; 127( 21): 215104-1-215104-7.Available from: https://doi.org/10.1063/5.0004395
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      Santos DP, Pelissari PIBGB, Mello RF de, Pandolfelli VC. Estimating the thermal insulating performance of multi-component refractory ceramic systems based on a machine learning surrogate model framework [Internet]. Journal of Applied Physics. 2020 ; 127( 21): 215104-1-215104-7.Available from: https://doi.org/10.1063/5.0004395
  • In: Sensors. Unidade: ICMC

    Subjects: Wireless, Algoritmos Genéticos

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      HAN, Dezhi; YU, Yunping; LI, Kuan-Ching; MELLO, Rodrigo Fernandes de. Enhancing the sensor node localization algorithm based on improved DV-Hop and DE algorithms in wireless sensor networks. Sensors, Basel, MDPI, v. 20, p. 1-24, 2020. Disponível em: < https://doi.org/10.3390/s20020343 > DOI: 10.3390/s20020343.
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      Han, D., Yu, Y., Li, K. -C., & Mello, R. F. de. (2020). Enhancing the sensor node localization algorithm based on improved DV-Hop and DE algorithms in wireless sensor networks. Sensors, 20, 1-24. doi:10.3390/s20020343
    • NLM

      Han D, Yu Y, Li K-C, Mello RF de. Enhancing the sensor node localization algorithm based on improved DV-Hop and DE algorithms in wireless sensor networks [Internet]. Sensors. 2020 ; 20 1-24.Available from: https://doi.org/10.3390/s20020343
    • Vancouver

      Han D, Yu Y, Li K-C, Mello RF de. Enhancing the sensor node localization algorithm based on improved DV-Hop and DE algorithms in wireless sensor networks [Internet]. Sensors. 2020 ; 20 1-24.Available from: https://doi.org/10.3390/s20020343
  • In: Journal of the Brazilian Computer Society. Unidade: ICMC

    Subjects: Redes Neurais, Aprendizado Computacional, Reconhecimento De Voz

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      SHULBY, Christopher D; FERREIRA, Martha D; MELLO, Rodrigo Fernandes de; ALUÍSIO, Sandra Maria. Theoretical learning guarantees applied to acoustic modeling. Journal of the Brazilian Computer Society, Heidelberg, SpringerOpen, v. 25, p. 1-12, 2019. Disponível em: < http://dx.doi.org/10.1186/s13173-018-0081-3 > DOI: 10.1186/s13173-018-0081-3.
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      Shulby, C. D., Ferreira, M. D., Mello, R. F. de, & Aluísio, S. M. (2019). Theoretical learning guarantees applied to acoustic modeling. Journal of the Brazilian Computer Society, 25, 1-12. doi:10.1186/s13173-018-0081-3
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      Shulby CD, Ferreira MD, Mello RF de, Aluísio SM. Theoretical learning guarantees applied to acoustic modeling [Internet]. Journal of the Brazilian Computer Society. 2019 ; 25 1-12.Available from: http://dx.doi.org/10.1186/s13173-018-0081-3
    • Vancouver

      Shulby CD, Ferreira MD, Mello RF de, Aluísio SM. Theoretical learning guarantees applied to acoustic modeling [Internet]. Journal of the Brazilian Computer Society. 2019 ; 25 1-12.Available from: http://dx.doi.org/10.1186/s13173-018-0081-3
  • In: Digital Signal Processing. Unidades: EP, ICMC

    Subjects: Análise De Séries Temporais, Métodos De Decomposição

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      DUARTE, Felipe Simões Lage Gomes; RIOS, Ricardo A; HRUSCHKA, Eduardo Raul; MELLO, Rodrigo Fernandes de. Decomposing time series into deterministic and stochastic influences: a survey. Digital Signal Processing, San Diego, Academic Press, v. 95, p. 1-18, 2019. Disponível em: < http://dx.doi.org/10.1016/j.dsp.2019.102582 > DOI: 10.1016/j.dsp.2019.102582.
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      Duarte, F. S. L. G., Rios, R. A., Hruschka, E. R., & Mello, R. F. de. (2019). Decomposing time series into deterministic and stochastic influences: a survey. Digital Signal Processing, 95, 1-18. doi:10.1016/j.dsp.2019.102582
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      Duarte FSLG, Rios RA, Hruschka ER, Mello RF de. Decomposing time series into deterministic and stochastic influences: a survey [Internet]. Digital Signal Processing. 2019 ; 95 1-18.Available from: http://dx.doi.org/10.1016/j.dsp.2019.102582
    • Vancouver

      Duarte FSLG, Rios RA, Hruschka ER, Mello RF de. Decomposing time series into deterministic and stochastic influences: a survey [Internet]. Digital Signal Processing. 2019 ; 95 1-18.Available from: http://dx.doi.org/10.1016/j.dsp.2019.102582
  • In: Expert Systems with Applications. Unidade: ICMC

    Subjects: Aprendizado Computacional, Matemática Da Computação

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      MELLO, Rodrigo Fernandes de; MANAPRAGADA, Chaitanya; BIFET, Albert. Measuring the shattering coefficient of decision tree models. Expert Systems with Applications, Kidlington, Pergamon, v. 137, p. 443-452, 2019. Disponível em: < http://dx.doi.org/10.1016/j.eswa.2019.07.012 > DOI: 10.1016/j.eswa.2019.07.012.
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      Mello, R. F. de, Manapragada, C., & Bifet, A. (2019). Measuring the shattering coefficient of decision tree models. Expert Systems with Applications, 137, 443-452. doi:10.1016/j.eswa.2019.07.012
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      Mello RF de, Manapragada C, Bifet A. Measuring the shattering coefficient of decision tree models [Internet]. Expert Systems with Applications. 2019 ; 137 443-452.Available from: http://dx.doi.org/10.1016/j.eswa.2019.07.012
    • Vancouver

      Mello RF de, Manapragada C, Bifet A. Measuring the shattering coefficient of decision tree models [Internet]. Expert Systems with Applications. 2019 ; 137 443-452.Available from: http://dx.doi.org/10.1016/j.eswa.2019.07.012
  • In: Expert Systems with Applications. Unidade: ICMC

    Subjects: Aprendizado Computacional, Análise De Séries Temporais

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      MELLO, Rodrigo Fernandes de; VAZ, Yule; FERREIRA, Carlos Henrique Grossi; BIFET, Albert. On learning guarantees to unsupervised concept drift detection on data streams. Expert Systems with Applications, Kidlington, Pergamon, v. 117, p. 90-102, 2019. Disponível em: < http://dx.doi.org/10.1016/j.eswa.2018.08.054 > DOI: 10.1016/j.eswa.2018.08.054.
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      Mello, R. F. de, Vaz, Y., Ferreira, C. H. G., & Bifet, A. (2019). On learning guarantees to unsupervised concept drift detection on data streams. Expert Systems with Applications, 117, 90-102. doi:10.1016/j.eswa.2018.08.054
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      Mello RF de, Vaz Y, Ferreira CHG, Bifet A. On learning guarantees to unsupervised concept drift detection on data streams [Internet]. Expert Systems with Applications. 2019 ; 117 90-102.Available from: http://dx.doi.org/10.1016/j.eswa.2018.08.054
    • Vancouver

      Mello RF de, Vaz Y, Ferreira CHG, Bifet A. On learning guarantees to unsupervised concept drift detection on data streams [Internet]. Expert Systems with Applications. 2019 ; 117 90-102.Available from: http://dx.doi.org/10.1016/j.eswa.2018.08.054
  • In: Proceedings. Conference title: IEEE International Conference on Big Data - Big Data. Unidade: ICMC

    Subjects: Aprendizado Computacional, Análise De Séries Temporais

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      GOMES, Heitor Murilo; MELLO, Rodrigo Fernandes de; PFAHRINGER, Bernhard; BIFET, Albert. Feature scoring using tree-based ensembles for evolving data streams. Anais.. Los Alamitos: IEEE, 2019.Disponível em: DOI: 10.1109/BigData47090.2019.9006366.
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      Gomes, H. M., Mello, R. F. de, Pfahringer, B., & Bifet, A. (2019). Feature scoring using tree-based ensembles for evolving data streams. In Proceedings. Los Alamitos: IEEE. doi:10.1109/BigData47090.2019.9006366
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      Gomes HM, Mello RF de, Pfahringer B, Bifet A. Feature scoring using tree-based ensembles for evolving data streams [Internet]. Proceedings. 2019 ;Available from: https://doi.org/10.1109/BigData47090.2019.9006366
    • Vancouver

      Gomes HM, Mello RF de, Pfahringer B, Bifet A. Feature scoring using tree-based ensembles for evolving data streams [Internet]. Proceedings. 2019 ;Available from: https://doi.org/10.1109/BigData47090.2019.9006366
  • In: Proceedings. Conference title: Symposium on Languages, Applications and Technologies - SLATE. Unidade: ICMC

    Subjects: Aprendizado Computacional, Algoritmos úteis E Específicos, Reconhecimento De Voz

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      SHULBY, Christopher Dane; FERREIRA, Martha Dais; MELLO, Rodrigo Fernandes de; ALUÍSIO, Sandra Maria. Robust phoneme recognition with little data. Anais.. Wadern: Dagstuhl Publishing, 2019.Disponível em: DOI: 10.4230/OASIcs.SLATE.2019.4.
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      Shulby, C. D., Ferreira, M. D., Mello, R. F. de, & Aluísio, S. M. (2019). Robust phoneme recognition with little data. In Proceedings. Wadern: Dagstuhl Publishing. doi:10.4230/OASIcs.SLATE.2019.4
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      Shulby CD, Ferreira MD, Mello RF de, Aluísio SM. Robust phoneme recognition with little data [Internet]. Proceedings. 2019 ;Available from: http://dx.doi.org/10.4230/OASIcs.SLATE.2019.4
    • Vancouver

      Shulby CD, Ferreira MD, Mello RF de, Aluísio SM. Robust phoneme recognition with little data [Internet]. Proceedings. 2019 ;Available from: http://dx.doi.org/10.4230/OASIcs.SLATE.2019.4
  • In: Expert Systems with Applications. Unidade: ICMC

    Subjects: Redes Neurais, Sistemas Dinâmicos, Aprendizado Computacional, Reconhecimento De Objetos

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      FERREIRA, Martha Dais; CORRÊA, Débora Cristina; NONATO, Luis Gustavo; MELLO, Rodrigo Fernandes de. Designing architectures of convolutional neural networks to solve practical problems. Expert Systems with Applications, Kidlington, Elsevier, v. 94, p. 205-217, 2018. Disponível em: < http://dx.doi.org/10.1016/j.eswa.2017.10.052 > DOI: 10.1016/j.eswa.2017.10.052.
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      Ferreira, M. D., Corrêa, D. C., Nonato, L. G., & Mello, R. F. de. (2018). Designing architectures of convolutional neural networks to solve practical problems. Expert Systems with Applications, 94, 205-217. doi:10.1016/j.eswa.2017.10.052
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      Ferreira MD, Corrêa DC, Nonato LG, Mello RF de. Designing architectures of convolutional neural networks to solve practical problems [Internet]. Expert Systems with Applications. 2018 ; 94 205-217.Available from: http://dx.doi.org/10.1016/j.eswa.2017.10.052
    • Vancouver

      Ferreira MD, Corrêa DC, Nonato LG, Mello RF de. Designing architectures of convolutional neural networks to solve practical problems [Internet]. Expert Systems with Applications. 2018 ; 94 205-217.Available from: http://dx.doi.org/10.1016/j.eswa.2017.10.052
  • In: Proceedings. Conference title: Conference on Graphics, Patterns and Images - SIBGRAPI. Unidade: ICMC

    Subjects: Aprendizado Computacional, Redes Neurais, Visão Computacional

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      NAZARÉ, Tiago S; COSTA, Gabriel B. Paranhos da; MELLO, Rodrigo Fernandes de; PONTI, Moacir Antonelli. Color quantization in transfer learning and noisy scenarios: an empirical analysis using convolutional networks. Anais.. Los Alamitos: IEEE, 2018.Disponível em: DOI: 10.1109/SIBGRAPI.2018.00055.
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      Nazaré, T. S., Costa, G. B. P. da, Mello, R. F. de, & Ponti, M. A. (2018). Color quantization in transfer learning and noisy scenarios: an empirical analysis using convolutional networks. In Proceedings. Los Alamitos: IEEE. doi:10.1109/SIBGRAPI.2018.00055
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      Nazaré TS, Costa GBP da, Mello RF de, Ponti MA. Color quantization in transfer learning and noisy scenarios: an empirical analysis using convolutional networks [Internet]. Proceedings. 2018 ;Available from: http://dx.doi.org/10.1109/SIBGRAPI.2018.00053
    • Vancouver

      Nazaré TS, Costa GBP da, Mello RF de, Ponti MA. Color quantization in transfer learning and noisy scenarios: an empirical analysis using convolutional networks [Internet]. Proceedings. 2018 ;Available from: http://dx.doi.org/10.1109/SIBGRAPI.2018.00053
  • In: International Journal of High Performance Computing and Networking. Unidade: ICMC

    Subjects: Aprendizado Computacional, Big Data, Reconhecimento De Padrões, Análise De Séries Temporais

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      ALBERTINI, Marcelo Keese; MELLO, Rodrigo Fernandes de. Estimating data stream tendencies to adapt clustering parameters. International Journal of High Performance Computing and Networking, Olney, Inderscience Publishers, v. 11, n. 1, p. 34-44, 2018. Disponível em: < http://dx.doi.org/10.1504/IJHPCN.2018.088877 > DOI: 10.1504/IJHPCN.2018.088877.
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      Albertini, M. K., & Mello, R. F. de. (2018). Estimating data stream tendencies to adapt clustering parameters. International Journal of High Performance Computing and Networking, 11( 1), 34-44. doi:10.1504/IJHPCN.2018.088877
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      Albertini MK, Mello RF de. Estimating data stream tendencies to adapt clustering parameters [Internet]. International Journal of High Performance Computing and Networking. 2018 ; 11( 1): 34-44.Available from: http://dx.doi.org/10.1504/IJHPCN.2018.088877
    • Vancouver

      Albertini MK, Mello RF de. Estimating data stream tendencies to adapt clustering parameters [Internet]. International Journal of High Performance Computing and Networking. 2018 ; 11( 1): 34-44.Available from: http://dx.doi.org/10.1504/IJHPCN.2018.088877
  • In: Journal of Volcanology and Geothermal Research. Unidade: ICMC

    Subjects: Vulcões, Ondas Sísmicas, Aprendizado Computacional, Reconhecimento De Padrões

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      CURILEM, Millaray; MELLO, Rodrigo Fernandes de; HUENUPAN, Fernando; et al. Discriminating seismic events of the Llaima volcano (Chile) based on spectrogram cross-correlations. Journal of Volcanology and Geothermal Research, Amsterdam, Elsevier, v. No 2018, p. 63-78, 2018. Disponível em: < http://dx.doi.org/10.1016/j.jvolgeores.2018.10.023 > DOI: 10.1016/j.jvolgeores.2018.10.023.
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      Curilem, M., Mello, R. F. de, Huenupan, F., San Martin, C., Franco, L., Hernández, E., & Rios, R. A. (2018). Discriminating seismic events of the Llaima volcano (Chile) based on spectrogram cross-correlations. Journal of Volcanology and Geothermal Research, No 2018, 63-78. doi:10.1016/j.jvolgeores.2018.10.023
    • NLM

      Curilem M, Mello RF de, Huenupan F, San Martin C, Franco L, Hernández E, Rios RA. Discriminating seismic events of the Llaima volcano (Chile) based on spectrogram cross-correlations [Internet]. Journal of Volcanology and Geothermal Research. 2018 ; No 2018 63-78.Available from: http://dx.doi.org/10.1016/j.jvolgeores.2018.10.023
    • Vancouver

      Curilem M, Mello RF de, Huenupan F, San Martin C, Franco L, Hernández E, Rios RA. Discriminating seismic events of the Llaima volcano (Chile) based on spectrogram cross-correlations [Internet]. Journal of Volcanology and Geothermal Research. 2018 ; No 2018 63-78.Available from: http://dx.doi.org/10.1016/j.jvolgeores.2018.10.023
  • Unidade: ICMC

    Subjects: Aprendizado Computacional

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      MELLO, Rodrigo Fernandes de; PONTI, Moacir Antonelli. Machine learning: a practical approach on the statistical learning theory. [S.l: s.n.], 2018.Disponível em: DOI: 10.1007/978-3-319-94989-5.
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      Mello, R. F. de, & Ponti, M. A. (2018). Machine learning: a practical approach on the statistical learning theory. Cham: Springer. doi:10.1007/978-3-319-94989-5
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      Mello RF de, Ponti MA. Machine learning: a practical approach on the statistical learning theory [Internet]. 2018 ;Available from: http://dx.doi.org/10.1007/978-3-319-94989-5
    • Vancouver

      Mello RF de, Ponti MA. Machine learning: a practical approach on the statistical learning theory [Internet]. 2018 ;Available from: http://dx.doi.org/10.1007/978-3-319-94989-5
  • In: Chaos. Unidade: ICMC

    Subjects: Aprendizado Computacional, Análise De Séries Temporais, Mídias Sociais

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    • ABNT

      MELLO, Rodrigo Fernandes de; RIOS, Ricardo A; PAGLIOSA, Paulo A. Concept drift detection on social network data using cross-recurrence quantification analysis. Chaos, Melville, AIP, v. 28, p. 085719-1-085719-15, 2018. Disponível em: < http://dx.doi.org/10.1063/1.5024241 > DOI: 10.1063/1.5024241.
    • APA

      Mello, R. F. de, Rios, R. A., & Pagliosa, P. A. (2018). Concept drift detection on social network data using cross-recurrence quantification analysis. Chaos, 28, 085719-1-085719-15. doi:10.1063/1.5024241
    • NLM

      Mello RF de, Rios RA, Pagliosa PA. Concept drift detection on social network data using cross-recurrence quantification analysis [Internet]. Chaos. 2018 ; 28 085719-1-085719-15.Available from: http://dx.doi.org/10.1063/1.5024241
    • Vancouver

      Mello RF de, Rios RA, Pagliosa PA. Concept drift detection on social network data using cross-recurrence quantification analysis [Internet]. Chaos. 2018 ; 28 085719-1-085719-15.Available from: http://dx.doi.org/10.1063/1.5024241
  • In: Pattern Recognition. Unidade: ICMC

    Subjects: Aprendizado Computacional, Reconhecimento De Padrões, Análise De Séries Temporais

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    A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
    • ABNT

      PAGLIOSA, Lucas de Carvalho; MELLO, Rodrigo Fernandes de. Semi-supervised time series classification on positive and unlabeled problems using cross-recurrence quantification analysis. Pattern Recognition, Kidlington, Elsevier, v. 80, p. 53-63, 2018. Disponível em: < http://dx.doi.org/10.1016/j.patcog.2018.02.030 > DOI: 10.1016/j.patcog.2018.02.030.
    • APA

      Pagliosa, L. de C., & Mello, R. F. de. (2018). Semi-supervised time series classification on positive and unlabeled problems using cross-recurrence quantification analysis. Pattern Recognition, 80, 53-63. doi:10.1016/j.patcog.2018.02.030
    • NLM

      Pagliosa L de C, Mello RF de. Semi-supervised time series classification on positive and unlabeled problems using cross-recurrence quantification analysis [Internet]. Pattern Recognition. 2018 ; 80 53-63.Available from: http://dx.doi.org/10.1016/j.patcog.2018.02.030
    • Vancouver

      Pagliosa L de C, Mello RF de. Semi-supervised time series classification on positive and unlabeled problems using cross-recurrence quantification analysis [Internet]. Pattern Recognition. 2018 ; 80 53-63.Available from: http://dx.doi.org/10.1016/j.patcog.2018.02.030


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