TY - NEWS TI - Deep learning architecture for the recursive patterns recognition model AU - Puerto, E AU - Aguilar, J AU - Reyes, J AU - Sarkar, D AB - 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. DA - 2018-12-07 PB - Journal of Physics: Conference Series UR - https://repositorio.ufps.edu.co/handle/ufps/1651 ER -