ReP USP - Resultado da busca

Trabalho de evento-resumo
Random projections and α-shape to support the kernel design (2020)
Cestari, Daniel Moreira ; Mello, Rodrigo Fernandes de
Source: Proceedings. Conference titles: AAAI Conference on Artificial Intelligence - AAAI. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, REDES NEURAIS, RECONHECIMENTO DE PADRÕES
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
CESTARI, Daniel Moreira e MELLO, Rodrigo Fernandes de. Random projections and α-shape to support the kernel design. 2020, Anais.. Palo Alto: AAAI Press, 2020. Disponível em: https://doi.org/10.1609/aaai.v34i10.7211. Acesso em: 27 nov. 2025.
APA
Cestari, D. M., & Mello, R. F. de. (2020). Random projections and α-shape to support the kernel design. In Proceedings. Palo Alto: AAAI Press. doi:10.1609/aaai.v34i10.7211
NLM
Cestari DM, Mello RF de. Random projections and α-shape to support the kernel design [Internet]. Proceedings. 2020 ;[citado 2025 nov. 27 ] Available from: https://doi.org/10.1609/aaai.v34i10.7211
Vancouver
Cestari DM, Mello RF de. Random projections and α-shape to support the kernel design [Internet]. Proceedings. 2020 ;[citado 2025 nov. 27 ] Available from: https://doi.org/10.1609/aaai.v34i10.7211
Trabalho de evento
DyS: a framework for mixture models in quantification (2019)
Maletzke, André Gustavo; Reis, Denis dos; Cherman, Everton Alvares; Batista, Gustavo Enrique de Almeida Prado Alves
Source: Proceedings. Conference titles: AAAI Conference on Artificial Intelligence - AAAI. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, MÉTODOS ESTATÍSTICOS PARA APRENDIZAGEM
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
MALETZKE, André Gustavo et al. DyS: a framework for mixture models in quantification. 2019, Anais.. Palo Alto: AAAI Press, 2019. Disponível em: https://doi.org/10.1609/aaai.v33i01.33014552. Acesso em: 27 nov. 2025.
APA
Maletzke, A. G., Reis, D. dos, Cherman, E. A., & Batista, G. E. de A. P. A. (2019). DyS: a framework for mixture models in quantification. In Proceedings. Palo Alto: AAAI Press. doi:10.1609/aaai.v33i01.33014552
NLM
Maletzke AG, Reis D dos, Cherman EA, Batista GE de APA. DyS: a framework for mixture models in quantification [Internet]. Proceedings. 2019 ;[citado 2025 nov. 27 ] Available from: https://doi.org/10.1609/aaai.v33i01.33014552
Vancouver
Maletzke AG, Reis D dos, Cherman EA, Batista GE de APA. DyS: a framework for mixture models in quantification [Internet]. Proceedings. 2019 ;[citado 2025 nov. 27 ] Available from: https://doi.org/10.1609/aaai.v33i01.33014552
Trabalho de evento
Graph construction for semi-supervised learning (2015)
Berton, Lilian; Lopes, Alneu de Andrade
Source: Proceedings. Conference titles: International Joint Conference on Artificial Intelligence - IJCAI. Unidade: ICMC
Subjects: INTELIGÊNCIA ARTIFICIAL, APRENDIZADO COMPUTACIONAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
BERTON, Lilian e LOPES, Alneu de Andrade. Graph construction for semi-supervised learning. 2015, Anais.. Palo Alto: AAAI Press, 2015. Disponível em: http://ijcai.org/papers15/Papers/IJCAI15-619.pdf. Acesso em: 27 nov. 2025.
APA
Berton, L., & Lopes, A. de A. (2015). Graph construction for semi-supervised learning. In Proceedings. Palo Alto: AAAI Press. Recuperado de http://ijcai.org/papers15/Papers/IJCAI15-619.pdf
NLM
Berton L, Lopes A de A. Graph construction for semi-supervised learning [Internet]. Proceedings. 2015 ;[citado 2025 nov. 27 ] Available from: http://ijcai.org/papers15/Papers/IJCAI15-619.pdf
Vancouver
Berton L, Lopes A de A. Graph construction for semi-supervised learning [Internet]. Proceedings. 2015 ;[citado 2025 nov. 27 ] Available from: http://ijcai.org/papers15/Papers/IJCAI15-619.pdf
Trabalho de evento
Bipartite graph for topic extraction (2015)
Faleiros, Thiago de Paulo; Lopes, Alneu de Andrade
Source: Proceedings. Conference titles: International Joint Conference on Artificial Intelligence - IJCAI. Unidade: ICMC
Subjects: INTELIGÊNCIA ARTIFICIAL, APRENDIZADO COMPUTACIONAL, MINERAÇÃO DE DADOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
FALEIROS, Thiago de Paulo e LOPES, Alneu de Andrade. Bipartite graph for topic extraction. 2015, Anais.. Palo Alto: AAAI Press, 2015. Disponível em: http://ijcai.org/papers15/Papers/IJCAI15-629.pdf. Acesso em: 27 nov. 2025.
APA
Faleiros, T. de P., & Lopes, A. de A. (2015). Bipartite graph for topic extraction. In Proceedings. Palo Alto: AAAI Press. Recuperado de http://ijcai.org/papers15/Papers/IJCAI15-629.pdf
NLM
Faleiros T de P, Lopes A de A. Bipartite graph for topic extraction [Internet]. Proceedings. 2015 ;[citado 2025 nov. 27 ] Available from: http://ijcai.org/papers15/Papers/IJCAI15-629.pdf
Vancouver
Faleiros T de P, Lopes A de A. Bipartite graph for topic extraction [Internet]. Proceedings. 2015 ;[citado 2025 nov. 27 ] Available from: http://ijcai.org/papers15/Papers/IJCAI15-629.pdf
Trabalho de evento
Stochastic density ratio estimation and its application to feature selection (2015)
Braga, Igor; Monard, Maria Carolina (Orientador) ; Vapnik, Vladimir (coorient)
Source: Proceedings. Conference titles: International Joint Conference on Artificial Intelligence - IJCAI. Unidade: ICMC
Subjects: INTELIGÊNCIA ARTIFICIAL, APRENDIZADO COMPUTACIONAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
BRAGA, Igor. Stochastic density ratio estimation and its application to feature selection. 2015, Anais.. Palo Alto: AAAI Press, 2015. Disponível em: http://ijcai.org/papers15/Papers/IJCAI15-620.pdf. Acesso em: 27 nov. 2025.
APA
Braga, I. (2015). Stochastic density ratio estimation and its application to feature selection. In Proceedings. Palo Alto: AAAI Press. Recuperado de http://ijcai.org/papers15/Papers/IJCAI15-620.pdf
NLM
Braga I. Stochastic density ratio estimation and its application to feature selection [Internet]. Proceedings. 2015 ;[citado 2025 nov. 27 ] Available from: http://ijcai.org/papers15/Papers/IJCAI15-620.pdf
Vancouver
Braga I. Stochastic density ratio estimation and its application to feature selection [Internet]. Proceedings. 2015 ;[citado 2025 nov. 27 ] Available from: http://ijcai.org/papers15/Papers/IJCAI15-620.pdf
Trabalho de evento
Feature selection for multi-label learning (2015)
Spolaôr, Newton; Monard, Maria Carolina ; Lee, Huei Diana
Source: Proceedings. Conference titles: International Joint Conference on Artificial Intelligence - IJCAI. Unidade: ICMC
Subjects: INTELIGÊNCIA ARTIFICIAL, APRENDIZADO COMPUTACIONAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
SPOLAÔR, Newton e MONARD, Maria Carolina e LEE, Huei Diana. Feature selection for multi-label learning. 2015, Anais.. Palo Alto: AAAI Press, 2015. Disponível em: http://ijcai.org/papers15/Papers/IJCAI15-648.pdf. Acesso em: 27 nov. 2025.
APA
Spolaôr, N., Monard, M. C., & Lee, H. D. (2015). Feature selection for multi-label learning. In Proceedings. Palo Alto: AAAI Press. Recuperado de http://ijcai.org/papers15/Papers/IJCAI15-648.pdf
NLM
Spolaôr N, Monard MC, Lee HD. Feature selection for multi-label learning [Internet]. Proceedings. 2015 ;[citado 2025 nov. 27 ] Available from: http://ijcai.org/papers15/Papers/IJCAI15-648.pdf
Vancouver
Spolaôr N, Monard MC, Lee HD. Feature selection for multi-label learning [Internet]. Proceedings. 2015 ;[citado 2025 nov. 27 ] Available from: http://ijcai.org/papers15/Papers/IJCAI15-648.pdf
Trabalho de evento
Semi-supervised learning and model search (2003)
Cozman, Fabio Gagliardi ; Cohen, Ira; Cirelo, Marcelo Cesar
Source: ICML-2003 : proceedings.. Conference titles: International Conference on Machine Learning. Unidade: EP
Subjects: APRENDIZADO COMPUTACIONAL, INTELIGÊNCIA ARTIFICIAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
COZMAN, Fabio Gagliardi e COHEN, Ira e CIRELO, Marcelo Cesar. Semi-supervised learning and model search. 2003, Anais.. Menlo Park, CA: AAAI Press, 2003. Disponível em: https://repositorio.usp.br/directbitstream/d50342b6-ec81-4054-a7b6-c8e04caa3ed2/Cozman-2003-semi%20supervised%20learning%20and%20model%20search.pdf. Acesso em: 27 nov. 2025.
APA
Cozman, F. G., Cohen, I., & Cirelo, M. C. (2003). Semi-supervised learning and model search. In ICML-2003 : proceedings.. Menlo Park, CA: AAAI Press. Recuperado de https://repositorio.usp.br/directbitstream/d50342b6-ec81-4054-a7b6-c8e04caa3ed2/Cozman-2003-semi%20supervised%20learning%20and%20model%20search.pdf
NLM
Cozman FG, Cohen I, Cirelo MC. Semi-supervised learning and model search [Internet]. ICML-2003 : proceedings. 2003 ;[citado 2025 nov. 27 ] Available from: https://repositorio.usp.br/directbitstream/d50342b6-ec81-4054-a7b6-c8e04caa3ed2/Cozman-2003-semi%20supervised%20learning%20and%20model%20search.pdf
Vancouver
Cozman FG, Cohen I, Cirelo MC. Semi-supervised learning and model search [Internet]. ICML-2003 : proceedings. 2003 ;[citado 2025 nov. 27 ] Available from: https://repositorio.usp.br/directbitstream/d50342b6-ec81-4054-a7b6-c8e04caa3ed2/Cozman-2003-semi%20supervised%20learning%20and%20model%20search.pdf
Trabalho de evento
Semi-supervised learning of mixture models (2003)
Cozman, Fabio Gagliardi ; Cohen, Marcelo; Cirelo, Marcelo Cesar
Source: ICML-2003 : proceedings.. Conference titles: International Conference on Machine Learning. Unidade: EP
Subjects: APRENDIZADO COMPUTACIONAL, INTELIGÊNCIA ARTIFICIAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
COZMAN, Fabio Gagliardi e COHEN, Marcelo e CIRELO, Marcelo Cesar. Semi-supervised learning of mixture models. 2003, Anais.. Menlo Park, CA: AAAI Press, 2003. Disponível em: https://repositorio.usp.br/directbitstream/3d0c5543-ac77-4538-b6b9-b621dd8288e8/Cozman-2003-Semi-supervised%20learning%20of%20mixture%20models.pdf. Acesso em: 27 nov. 2025.
APA
Cozman, F. G., Cohen, M., & Cirelo, M. C. (2003). Semi-supervised learning of mixture models. In ICML-2003 : proceedings.. Menlo Park, CA: AAAI Press. Recuperado de https://repositorio.usp.br/directbitstream/3d0c5543-ac77-4538-b6b9-b621dd8288e8/Cozman-2003-Semi-supervised%20learning%20of%20mixture%20models.pdf
NLM
Cozman FG, Cohen M, Cirelo MC. Semi-supervised learning of mixture models [Internet]. ICML-2003 : proceedings. 2003 ;[citado 2025 nov. 27 ] Available from: https://repositorio.usp.br/directbitstream/3d0c5543-ac77-4538-b6b9-b621dd8288e8/Cozman-2003-Semi-supervised%20learning%20of%20mixture%20models.pdf
Vancouver
Cozman FG, Cohen M, Cirelo MC. Semi-supervised learning of mixture models [Internet]. ICML-2003 : proceedings. 2003 ;[citado 2025 nov. 27 ] Available from: https://repositorio.usp.br/directbitstream/3d0c5543-ac77-4538-b6b9-b621dd8288e8/Cozman-2003-Semi-supervised%20learning%20of%20mixture%20models.pdf
Trabalho de evento
Unlabeled data can degrade classification performance of generative classifiers (2002)
Cozman, Fabio Gagliardi ; Cohen, Ira
Source: FLAIRS 2002 : proceedings. Conference titles: International Florida Articial Intelligence Research Society Conference. Unidade: EP
Subjects: CLASSIFICAÇÃO (DANOS), APRENDIZADO COMPUTACIONAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
COZMAN, Fabio Gagliardi e COHEN, Ira. Unlabeled data can degrade classification performance of generative classifiers. 2002, Anais.. Menlo Park, California: AAAI Press, 2002. Disponível em: https://repositorio.usp.br/directbitstream/66d32e74-0b28-4fc0-92bb-13bce47ea766/Cozman-2002-Unlabeled%20data%20can%20degrade%20classification%20performance.pdf. Acesso em: 27 nov. 2025.
APA
Cozman, F. G., & Cohen, I. (2002). Unlabeled data can degrade classification performance of generative classifiers. In FLAIRS 2002 : proceedings. Menlo Park, California: AAAI Press. Recuperado de https://repositorio.usp.br/directbitstream/66d32e74-0b28-4fc0-92bb-13bce47ea766/Cozman-2002-Unlabeled%20data%20can%20degrade%20classification%20performance.pdf
NLM
Cozman FG, Cohen I. Unlabeled data can degrade classification performance of generative classifiers [Internet]. FLAIRS 2002 : proceedings. 2002 ;[citado 2025 nov. 27 ] Available from: https://repositorio.usp.br/directbitstream/66d32e74-0b28-4fc0-92bb-13bce47ea766/Cozman-2002-Unlabeled%20data%20can%20degrade%20classification%20performance.pdf
Vancouver
Cozman FG, Cohen I. Unlabeled data can degrade classification performance of generative classifiers [Internet]. FLAIRS 2002 : proceedings. 2002 ;[citado 2025 nov. 27 ] Available from: https://repositorio.usp.br/directbitstream/66d32e74-0b28-4fc0-92bb-13bce47ea766/Cozman-2002-Unlabeled%20data%20can%20degrade%20classification%20performance.pdf

USP Schools

ReP

Filtros

Authors

USP affiliated authors

Subjects

Funding Agencies