Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Jarley P. Nobrega"'
Publikováno v:
Neurocomputing. 337:235-250
In many regression and time series forecasting problems, the input data is not fully available at the beginning of the training phase. Conventional machine learning methods for batch data are not able to handle this problem. The sequential version of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b4abca0630bb33b7a03874eca0350fa0
https://doi.org/10.1016/j.neucom.2019.01.070
https://doi.org/10.1016/j.neucom.2019.01.070
Autor:
Rodrigo C. Brasileiro, Adriano L. I. Oliveira, Victor L. F. Souza, Jarley P. Nobrega, Rodolfo C. Cavalcante
Publikováno v:
Expert Systems with Applications. 55:194-211
We propose a survey of soft computing techniques applied to financial market.We surveyed several primary studies proposed in the literature.A framework for building an intelligent trading system was proposed.Future directions of this research field a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::31a98cabc64b974eb99829e82e0a0b81
https://doi.org/10.1016/j.eswa.2016.02.006
https://doi.org/10.1016/j.eswa.2016.02.006
Publikováno v:
Engineering Applications of Artificial Intelligence. 44:101-110
In this paper, a new sequential learning algorithm is constructed by combining the Online Sequential Extreme Learning Machine (OS-ELM) and Kalman filter regression. The Kalman Online Sequential Extreme Learning Machine (KOSELM) handles the problem of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f80eadce9ac8134a7f57a5376bd6263a
https://doi.org/10.1016/j.engappai.2015.05.010
https://doi.org/10.1016/j.engappai.2015.05.010
Publikováno v:
ICTAI
Software development effort estimation is the process of predicting the effort required to develop a software system. In order to improve the estimation accuracy, many different models have been proposed in the literature. Multiple classification sys
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::66e84ebfcd1b28ea6be4598759563e1b
https://doi.org/10.1109/ictai.2017.00042
https://doi.org/10.1109/ictai.2017.00042
Publikováno v:
SMC
In this paper we evaluate the combination of Extreme Learning Machine (ELM) and Support Vector Regression (SVR) with a Kalman filter regression model for financial time series forecasting. We also compare the forecast performance with a set of linear
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::542a88b3ced069f7dd6743454d9559b9
https://doi.org/10.1109/smc.2014.6974093
https://doi.org/10.1109/smc.2014.6974093
Publikováno v:
ICTAI
In this paper we investigate the statistical and economic performance for statistical arbitrage strategy using Extreme Learning Machine (ELM) and Support Vector Regression (SVR) models, and their forecast combination through four linear combination m
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::58c85a1645caf743e3078a65907b8b22
https://doi.org/10.1109/ictai.2013.36
https://doi.org/10.1109/ictai.2013.36
Publikováno v:
Artificial Neural Networks and Machine Learning – ICANN 2012 ISBN: 9783642332654
ICANN (2)
ICANN (2)
It is increasingly common to use tools of Symbolic Data Analysis to reduce the data set without losing much information. Moreover, symbolic variables can be used to preserving the privacy of individuals when their information are present in the data
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3717b70cceab4d7796fba20afcbfe152
https://doi.org/10.1007/978-3-642-33266-1_54
https://doi.org/10.1007/978-3-642-33266-1_54
Publikováno v:
EUROMICRO-SEAA
A large number of software organizations are adopting the software product line approach in their reuse program. One fundamental factor to evaluate cost-benefit of this approach is the practical use of cost models to estimate if an investment is wort
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3dcee0101b4f286233ac3a2cf1c2894d
https://doi.org/10.1109/seaa.2008.41
https://doi.org/10.1109/seaa.2008.41