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.
|Original language||English (US)|
|Journal||Journal of Physics: Conference Series|
|State||Published - Dec 7 2018|
|Event||4th International Meeting on Applied Sciences and Engineering, EISI 2018 - San Jose de Cucuta, Colombia|
Duration: Sep 3 2018 → Sep 7 2018
ASJC Scopus subject areas
- Physics and Astronomy(all)