Evolving long short-term memory networks

• Autor(a) principal: Lobo Neto, Vicente Coelho [UNESP]
• Data de Publicação: 2020
• Outros Autores: Passos, Leandro Aparecido [UNESP], Papa, João Paulo [UNESP]
• Tipo de documento: Artigo de conferência
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1007/978-3-030-50417-5_25; http://hdl.handle.net/11449/233010
• topic: Evolutionary algorithms; Long Short-Term Memory; Metaheuristic optimization; Part-of-Speech tagging

Machine learning techniques have been massively employed in the last years over a wide variety of applications, especially those based on deep learning, which obtained state-of-the-art results in several research fields. Despite the success, such techniques still suffer from some shortcomings, such as the sensitivity to their hyperparameters, whose proper selection is context-dependent, i.e., the model may perform better over each dataset when using a specific set of hyperparameters. Therefore, we propose an approach based on evolutionary optimization techniques for fine-tuning Long Short-Term Memory networks. Experiments were conducted over three public word-processing datasets for part-of-speech tagging. The results showed the robustness of the proposed approach for the aforementioned task.