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Ensemble learning in recommender systems: combining multiple user interactions for ranking personalization
(Universidade Federal da Paraíba - UFPBNúcleo de Pesquisa e Extensão em Aplicações de Vídeo Digital - LAViDSociedade Brasileira de Computação - SBCJoão Pessoa, 2014-11)
In this paper, we propose a technique that uses multimodal interactions of users to generate a more accurate list of recommendations optimized for the user . Our approach is a response to the actual scenario on the Web ...
Learning Ensembles of Neural Networks by Means of a Bayesian Artificial Immune System
(Ieee-inst Electrical Electronics Engineers IncPiscatawayEUA, 2011)
Convolutional neural networks ensembles through single-iteration optimization
(2022-01-01)
Convolutional Neural Networks have been widely employed in a diverse range of computer vision-based applications, including image classification, object recognition, and object segmentation. Nevertheless, one weakness of ...
Electrical load prediction of healthcare buildings through single and ensemble learning
Healthcare buildings are characterized by complex energy systems and high energy usage, therefore
serving as the key areas for achieving energy conservation goals in the building sector. An accurate
load prediction of ...
ICE: A visual analytic tool for interactive clustering ensembles generation
(Association for Computing MachineryPE, 2021)
"Clustering methods are the most used algorithms for unsupervised learning. However, there is no unique optimal approach for all datasets since different clustering algorithms produce different partitions. To overcome this ...
Self-poised ensemble learning
(SPRINGER, 2005)
Ensemble learning with local diversity
(SPRINGER, 2006)
An optimization framework for combining ensembles of classifiers and clusterers with applications to nontransductive semisupervised learning and transfer learning.
(Association for Computing Machinery - ACMNew York, 2014-08)
Unsupervised models can provide supplementary soft constraints to help classify new “target” data because similar instances in the target set are more likely to share the same class label. Such models can also help detect ...
Time series forecasting based on ensemble learning methods applied to agribusiness, epidemiology, energy demand, and renewable energy
(Pontifícia Universidade Católica do ParanáPato BrancoBrasilPrograma de Pós-Graduação em Egenharia de Produção e SistemasPUCPR, 2021-12-03)
Time series forecasting and analysis are helpful in the decision-making process. However, exogenous factors, nonlinearities, and seasonality make developing efficient forecasting models challenging. In this context, the ...
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 ...