Regularization of extreme learning machines with information of spatial relations of the projected data
| dc.creator | Lourenço Ribeiro Grossi Araújo | |
| dc.creator | Luiz C. B. Torres | |
| dc.creator | Carla Caldeira Takahashi | |
| dc.creator | Antônio de Pádua Braga | |
| dc.creator | Leonardo J. Silvestre | |
| dc.date.accessioned | 2025-05-13T15:08:57Z | |
| dc.date.accessioned | 2025-09-08T22:54:32Z | |
| dc.date.available | 2025-05-13T15:08:57Z | |
| dc.date.issued | 2019 | |
| dc.identifier.doi | 10.1109/CoDIT.2019.8820610 | |
| dc.identifier.uri | https://hdl.handle.net/1843/82230 | |
| dc.language | eng | |
| dc.publisher | Universidade Federal de Minas Gerais | |
| dc.relation.ispartof | 6th International Conference on Control, Decision and Information Technologies (CoDIT) | |
| dc.rights | Acesso Restrito | |
| dc.subject | Redes neurais (Computação) | |
| dc.subject.other | Neurons , Training , Cybernetics , Minimization , Testing , Biological neural networks , Aerospace electronics | |
| dc.subject.other | Extreme Learning Machine , Training Time , Parameter Selection , Weight Decay , Regularization Parameter , Null Hypothesis , High-dimensional , Dimensional Space , Hidden Layer , Weight Matrix , Linear Discriminant Analysis , Weight Vector , Decision Boundary , Neurons In The Hidden Layer , Hidden Neurons , Affinity Matrix , Linearly Separable , Regularization Scheme , Class Mean , Universal Approximation , Norm Penalty , Fisher Score , Single Layer Feedforward Network , Empirical Risk Minimization | |
| dc.title | Regularization of extreme learning machines with information of spatial relations of the projected data | |
| dc.type | Artigo de evento | |
| local.description.resumo | The following work presents a new approach to automatic selection of Tikhonov's regularization parameter, responsible for controlling the weight value of an ELM neural network. A strategy based on measurements obtained from data projection (Fisher-Score) is introduced. Seven datasets are tested and results are compared to those obtained when the regularization parameter is selected through cross-validation. The strategy shows satisfactory classification performance (in terms of p-value), while presenting significant training time reduction. | |
| local.publisher.country | Brasil | |
| local.publisher.department | ENG - DEPARTAMENTO DE ENGENHARIA ELETRÔNICA | |
| local.publisher.initials | UFMG | |
| local.url.externa | https://ieeexplore.ieee.org/document/8820610 |
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