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Stock closing price forecasting using ensembles of constructive neural networks
(2014-01-01)
Efficient automatic systems which continuously learn over long periods of time, and manage to evolve its knowledge, by discarding obsolete parts of it and acquiring new ones to reflect recent data, are difficult to be ...
Desenvolvimento de estratégias e fenômenos em dinâmicas de jogos de múltiplos agentes
(2020-11)
Recent developments in Reinforcement Learning (RL) methods are focused on models that can learn good policies in non stationary environments, such as multi-agent games, where agents must learn how to react to changes in ...
Strategic interactions against non-stationary agents
(Instituto Nacional de Astrofísica, Óptica y Electrónica, 2015)
Strategic interactions against non-stationary agents
(Instituto Nacional de Astrofísica, Óptica y Electrónica, 2015)
Algorithm recommendation for data streams
(2020-01-01)
In the last decades, many companies have taken advantage of knowledge discovery to identify valuable information in massive volumes of data generated at high frequency. Machine learning techniques can be employed for ...
Learning concept drift with ensembles of optimum-path forest-based classifiers
(Elsevier B.V., 2019-06-01)
Concept drift methods learn patterns in non-stationary environments. Although such behavior is usually not expected in traditional classification problems, in real-world scenarios one can face them very much easier. In ...
Experiências com variações prequential para avaliação da aprendizagem em fluxo de dados
(Universidade Federal de PernambucoUFPEBrasilPrograma de Pos Graduacao em Ciencia da Computacao, 2018)
Programação em lógica não-monotônica aplicada à redução do espaço de planos em processos de decisão de Markov
(Centro Universitário FEI, São Bernardo do Campo, 2016)
Um desafio presente em problemas de tomada de decisão sequencial é o fato de que, ao longo do tempo, um domínio pode sofrer alterações não previstas. Enquanto que descrever apenas o domínio atual faz com que a chance de ...
Aprendizado de mudança de conceito por floresta de caminhos ótimos
(Universidade Federal de São CarlosUFSCarPrograma de Pós-Graduação em Ciência da Computação - PPGCCCâmpus São Carlos, 2020-12-17)
Classification algorithms take their decisions according to a learning process on the training set. Therefore, the data to be classified in the test set must have the same distribution as the training set to be correctly ...