Fine-tuning restricted Boltzmann machines using quaternion-based flower pollination algorithm
Recent works spotlighted machine learning as the technology of the new century. One can see its fantastic capability in solving problems throughout several domains, ranging from computer vision to natural language processing tasks. Nevertheless, there is still a burden when designing a new architect...
| Autores: | , , , |
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| Tipo de recurso: | capítulo de libro |
| Estado: | Versión publicada |
| Fecha de publicación: | 2020 |
| País: | Brasil |
| Institución: | Universidade Estadual Paulista (UNESP) |
| Repositorio: | Repositório Institucional da UNESP |
| Idioma: | inglés |
| OAI Identifier: | oai:repositorio.unesp.br:11449/309923 |
| Acceso en línea: | http://dx.doi.org/10.1016/B978-0-12-819714-1.00019-1 https://hdl.handle.net/11449/309923 |
| Access Level: | acceso abierto |
| Palabra clave: | flower pollination algorithm metaheuristic optimization quaternionic hypercomplex representation restricted Boltzmann machine |
| Sumario: | Recent works spotlighted machine learning as the technology of the new century. One can see its fantastic capability in solving problems throughout several domains, ranging from computer vision to natural language processing tasks. Nevertheless, there is still a burden when designing a new architecture, mainly due to its parameter setting up phase, which is often performed empirically. In the pursuit of mitigating this issue, optimization techniques have risen and taken the lead, being capable of providing feasible solutions to the problem. In this work, we introduce a quaternion-based flower pollination algorithm to fine-tune restricted Boltzmann machines. The effectiveness of the proposed approach is compared with several metaheuristic techniques, from naïve versions to quaternion-based ones. The experiments were carried out in three public datasets available in the literature and demonstrated promising results when compared with other techniques. |
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