Zobrazeno 1 - 10
of 26
pro vyhledávání: '"Bruno Almeida Pimentel"'
Publikováno v:
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems. 30:567-594
Fuzzy C-means (FCM) clustering algorithm is an important and popular clustering algorithm which is utilized in various application domains such as pattern recognition, machine learning, and data mining. Although this algorithm has shown acceptable pe
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9da518bc3c1509ce92aaf86e076567d4
https://doi.org/10.1142/s0218488522500143
https://doi.org/10.1142/s0218488522500143
Autor:
Leandro L. Minku, George G. Cabral, Marcella Martins, Markus Wagner, Sara Ceschia, Luca Di Gaspero, Andrea Schaerf, Alberto Franzin, Thomas Stutzle, Diego Oliva, Alfonso Ramos-Michel, Mario A. Navarro, Eduardo H. Haro, Angel Casas, Roberto Santana, Amanda Cristina Fraga De Albuquerque, Brendon Erick Euzebio Rus Peres, Erikson Freitas de Morais, Gilson Junior Soares, Jose Lohame Capinga, Lucas Costa, Marcio Guerreiro, Erickson Puchta, Yara de Souza Tadano, Thiago Antonini Alves, Mauricio Kaster, Hugo Valadares Siqueira, Harish Tayyar Madabushi, Shuo Wang, Lina Yao, Bruno Almeida Pimentel, Valmir Macario
First Edition of the IEEE CIS Open Book on Introduction to Computational Intelligence - Volume 1.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d6d8e484a154f66a993559a3a6021b61
Publikováno v:
2022 International Joint Conference on Neural Networks (IJCNN).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3f2736340e35301155a52ccde315a7f3
https://doi.org/10.1109/ijcnn55064.2022.9892394
https://doi.org/10.1109/ijcnn55064.2022.9892394
Autor:
Bruno Almeida Pimentel, Tiago Botari, André C. P. L. F. de Carvalho, Saulo Martiello Mastelini, Daniel R. Cassar, Edesio Alcobaça, Edgar Dutra Zanotto
Publikováno v:
Repositório Institucional da USP (Biblioteca Digital da Produção Intelectual)
Universidade de São Paulo (USP)
instacron:USP
Universidade de São Paulo (USP)
instacron:USP
Modern technologies demand the development of new glasses with unusual properties. Most of the previous developments occurred by slow, expensive trial-and-error approaches, which have produced a considerable amount of data over the past 100 years. By
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3f34a09e2c18825866de5726dc04f8dc
Autor:
Francisco Louzada, Taiza E. G. Santos, Diego C. Nascimento, Dylan J. Edwards, João Pereira Leite, Renata M. C. R. de Souza, Bruno Almeida Pimentel
Publikováno v:
Repositório Institucional da USP (Biblioteca Digital da Produção Intelectual)
Universidade de São Paulo (USP)
instacron:USP
Universidade de São Paulo (USP)
instacron:USP
This work aimed to appraise a multivariate time series , high-dimensionality data-set, presented as intervals using a Symbolic Data Analysis (SDA) approach. SDA reduces data dimensionality, considering the complexity of the model information through
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2dc5417031491eb3b29f475b8d5d0795
Publikováno v:
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems. 26:893-916
Fuzzy c-Means (FCM) and Possibilistic c-Means (PCM) are the most popular algorithms of the fuzzy and possibilistic clustering approaches, respectively. A hybridization of these methods, called Possibilistic Fuzzy c-Means (PFCM), solves noise sensitiv
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6d5748994d05d4c71a0b30f596f3c5aa
https://doi.org/10.1142/s021848851850040x
https://doi.org/10.1142/s021848851850040x
Publikováno v:
IJCNN
In this work, the goal is to use clustering algorithms as recommender in a meta-learning system and, thus, to propose an unsupervised meta-learning approach. Meta-learning has been successfully used for recommendation of Machine Learning algorithms i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d27944d8bbaef608580b3925327a4c63
https://doi.org/10.1109/ijcnn.2019.8851989
https://doi.org/10.1109/ijcnn.2019.8851989
Autor:
Carlos W.D. de Almeida, Rodrigo B. de C. Cavalcanti, Bruno Almeida Pimentel, Renata M. C. R. de Souza
Publikováno v:
IJCNN
Usually, in a fuzzy clustering, the memberships are the same for all the variables (features), i.e., the variables are considered equally important for the definition of the memberships. Fuzzy Kohonen Clustering network (FKCN) is a self-organizing fu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8e3d24e923098864bfcedd8c8838470c
https://doi.org/10.1109/ijcnn.2019.8852243
https://doi.org/10.1109/ijcnn.2019.8852243
Publikováno v:
Repositório Institucional da USP (Biblioteca Digital da Produção Intelectual)
Universidade de São Paulo (USP)
instacron:USP
Universidade de São Paulo (USP)
instacron:USP
Meta-learning has been successfully used for algorithm recommendation tasks. It uses machine learning to induce meta-models able to predict the best algorithms for a new dataset. In this paper, meta-models are applied to a set of meta-features, descr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fc5be0b604eaebaa00818259d66b3f0b
Publikováno v:
Neurocomputing. 174:946-965
With the growing interest in automatic understanding, processing and summarization of data, several application domains, such as pattern recognition, machine learning and computational biology, have been making use of clustering algorithms. In the fu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1eab2a01f45a9e32a997ea805d50ffc8
https://doi.org/10.1016/j.neucom.2015.10.011
https://doi.org/10.1016/j.neucom.2015.10.011