Deep learning architecture for the recursive patterns recognition model
In this work, we propose a deep learning approach for the recursive pattern recognition model, called AR2P (for its acronym in Spanish: "Algoritmo Recursivo de Reconocimiento de Patrones"), by extending its supervised learning capability towards a semi-supervised learning scheme. The deep...
- Autores:
-
Puerto, E
Aguilar, J
Reyes, J
Sarkar, D
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2018
- Institución:
- Universidad Francisco de Paula Santander
- Repositorio:
- Repositorio Digital UFPS
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.ufps.edu.co:ufps/1651
- Acceso en línea:
- http://repositorio.ufps.edu.co/handle/ufps/1651
https://doi.org/10.1088/1742-6596/1126/1/012035
- Palabra clave:
- Rights
- openAccess
- License
- © Copyright 2021 IOP Publishing
| Summary: | In this work, we propose a deep learning approach for the recursive pattern recognition model, called AR2P (for its acronym in Spanish: "Algoritmo Recursivo de Reconocimiento de Patrones"), by extending its supervised learning capability towards a semi-supervised learning scheme. The deep learning architecture is composed of three phases: the first one, called discovery phase, discovers the atomic descriptors. The second one, called aggregation phase, creates a feature hierarchy (merge of descriptors) from atomic descriptors. Finally, the classification phase carries out the classification of the inputs based on the feature hierarchy. The last phase uses a supervised learning approach, while the first two follow an unsupervised learning approach. In this paper is tested the performance of the proposed model, using a dataset from the UCI Machine Learning Repository. |
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