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Data preparation pipeline recommendation via meta-learning
(Universidade Federal de São CarlosUFSCarPrograma de Pós-Graduação em Ciência da Computação - PPGCCCâmpus São Carlos, 2021-05-26)
Data preparation is a essential stage in the machine learning pipeline, aiming to convert noisy and disordered data into refined data compatible with the algorithms. However, data preparation is time-consuming and requires ...
Uma abordagem para a escolha do melhor método de seleção de instâncias usando meta-aprendizagem
(Universidade Federal de PernambucoUFPEBrasilPrograma de Pos Graduacao em Ciencia da Computacao, 2016)
A meta-learning recommender system for hyperparameter tuning: Predicting when tuning improves SVM classifiers
(Elsevier B.V., 2019-10-01)
For many machine learning algorithms, predictive performance is critically affected by the hyperparameter values used to train them. However, tuning these hyperparameters can come at a high computational cost, especially ...
Meta-learning recommendation of default hyper-parameter values for SVMs in classifications tasks
(2015-01-01)
Machine learning algorithms have been investigated in several scenarios, one of them is the data classification. The predictive performance of the models induced by these algorithms is usually strongly affected by the ...
A guidance of data stream characterization for meta-learning
(2017-01-01)
The problem of selecting learning algorithms has been studied by the meta-learning community for more than two decades. One of the most important task for the success of a meta-learning system is gathering data about the ...
Single, multi- and many-objective meta-heuristic algorithms applied to pattern recognition
(Universidade Federal de São CarlosUFSCarPrograma de Pós-Graduação em Ciência da Computação - PPGCCCâmpus São Carlos, 2019-07-10)
In the last few years, metaheuristic algorithms have been used for solving several problems in engineering, biology, physics, among others, since many of them can be modeled as being optimization tasks. Metaheuristic methods ...
Fine-Tuning Dropout Regularization in Energy-Based Deep Learning
(2021-01-01)
Deep Learning architectures have been extensively studied in the last years, mainly due to their discriminative power in Computer Vision. However, one problem related to such models concerns their number of parameters and ...
Fine Tuning Deep Boltzmann Machines Through Meta-Heuristic Approaches
(Ieee, 2018-01-01)
The Deep learning framework has been widely used in different applications from medicine to engineering. However, there is a lack of works that manage to deal with the issue of hyperparameter fine-tuning, since machine ...
Combining meta-learning and search techniques to select parameters for support vector machines
(ELSEVIER SCIENCE BVAMSTERDAM, 2012)
Support Vector Machines (SVMs) have achieved very good performance on different learning problems. However, the success of SVMs depends on the adequate choice of the values of a number of parameters (e.g., the kernel and ...