Zobrazeno 1 - 10 of 20 pro vyhledávání: '"Diego P. P. Mesquita"'
1
A sparse linear regression model for incomplete datasets
Autor: João P. P. Gomes, César Lincoln C. Mattos, Marcelo B. A. Veras, Diego P. P. Mesquita
Publikováno v: Pattern Analysis and Applications. 23:1293-1303
Incomplete data are often neglected when designing machine learning methods. A popular strategy adopted by practitioners to circumvent this consists of taking a preprocessing step to fill the missing components. These preprocessing algorithms are des
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::4952544e8e5b6d3f619fc43131bd705b
https://doi.org/10.1007/s10044-019-00859-3
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2
Artificial Neural Networks with Random Weights for Incomplete Datasets
Autor: João P. P. Gomes, Leonardo Ramos Rodrigues, Diego P. P. Mesquita
Publikováno v: Neural Processing Letters. 50:2345-2372
In this paper, we propose a method to design Neural Networks with Random Weights in the presence of incomplete data. We present a method, under the general assumption that the data is missing-at-random, to estimate the weights of the output layer as
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::b34c3618952b5c8e062fc41f2a078cf0
https://doi.org/10.1007/s11063-019-10012-0
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3
Gaussian kernels for incomplete data
Autor: Amauri H. Souza Júnior, Diego P. P. Mesquita, João P. P. Gomes, Francesco Corona, Juvêncio S. Nobre
Publikováno v: APPLIED SOFT COMPUTING. 77:356-365
This paper discusses a method to estimate the expected value of the Gaussian kernel in the presence of incomplete data. We show how, under the general assumption of a missing-at-random mechanism, the expected value of the Gaussian kernel function has
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::89196e30570ccdfe6c1b8928d55d4105
http://juuli.fi/Record/0338012019
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4
How do loss functions impact the performance of graph neural networks?
Autor: Gabriel Jonas Duarte, Diego P. P. Mesquita, Erik Jhones Freitas do Nascimento, Amauri H. Souza Júnior, Tamara Arruda Pereira
Publikováno v: Anais do 15. Congresso Brasileiro de Inteligência Computacional.
Graph neural networks (GNNs) have become the de facto approach for supervised learning on graph data.To train these networks, most practitioners employ the categorical cross-entropy (CE) loss. We can attribute this largely to the probabilistic interp
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::7e0904e15b036103a9dada55231c4b32
https://doi.org/10.21528/cbic2021-161
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6
Building selective ensembles of Randomization Based Neural Networks with the successive projections algorithm
Autor: Leonardo Ramos Rodrigues, Roberto Kawakami Harrop Galvão, João P. P. Gomes, Diego P. P. Mesquita, Saulo A. F. Oliveira
Publikováno v: Applied Soft Computing. 70:1135-1145
Randomization based methods for training neural networks have gained increasing attention in recent years and achieved remarkable performances on a wide variety of tasks. The interest in such methods relies on the fact that standard gradient based le
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::fb56457ade0af9896aa95d43a382346f
https://doi.org/10.1016/j.asoc.2017.08.007
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7
Fast Co-MLM: An Efficient Semi-supervised Co-training Method Based on the Minimal Learning Machine
Autor: Diego P. P. Mesquita, João P. P. Gomes, Weslley L. Caldas
Publikováno v: New Generation Computing. 36:41-58
Co-training is a framework for semi-supervised learning that has attracted much attention due to its good performance and easy adaptation for various learning algorithms. In a recent work, Caldas et al. proposed a co-training-based method using the r
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::7f1f4d853dffcaa69488b7d564a105a8
https://doi.org/10.1007/s00354-017-0027-x
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8
Euclidean distance estimation in incomplete datasets
Autor: Juvncio S. Nobre, Joo Paulo P. Gomes, Diego P. P. Mesquita, Amauri H. Souza Júnior
Publikováno v: Neurocomputing. 248:11-18
This paper proposes a method to estimate the expected value of the Euclidean distance between two possibly incomplete feature vectors. Under the Missing at Random assumption, we show that the Euclidean distance can be modeled by a Nakagami distributi
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::c318a4b5dd347470158fe9da027c4859
https://doi.org/10.1016/j.neucom.2016.12.081
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9
Ensemble of Efficient Minimal Learning Machines for Classification and Regression
Autor: João P. P. Gomes, Diego P. P. Mesquita, Amauri H. Souza Júnior
Publikováno v: Neural Processing Letters. 46:751-766
Minimal Learning Machine (MLM) is a recently proposed supervised learning algorithm with performance comparable to most state-of-the-art machine learning methods. In this work, we propose ensemble methods for classification and regression using MLMs.
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::267d07c5f6852145d1882b25e5e3f4be
https://doi.org/10.1007/s11063-017-9587-5
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10
Classification with reject option for software defect prediction
Autor: Diego P. P. Mesquita, Lincoln S. Rocha, Joo Paulo P. Gomes, Ajalmar R. da Rocha Neto
Publikováno v: Applied Soft Computing. 49:1085-1093
Graphical abstractDisplay Omitted HighlightsWe propose the use of classification with reject option for software defect prediction (SDP) as a way to incorporate additional knowledge in the SDP process.We propose two variants of the extreme learning m
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::73eb88d3a93d791bce4ffaa8fe2f2a6a
https://doi.org/10.1016/j.asoc.2016.06.023
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