Deep learning architecture for the recursive patterns recognition model

E. Puerto, J. Aguilar, J. Reyes, D. Sarkar

Research output: Contribution to journalConference articlepeer-review

2 Scopus citations

Abstract

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 languageEnglish (US)
Article number012035
JournalJournal of Physics: Conference Series
Volume1126
Issue number1
DOIs
StatePublished - Dec 7 2018
Event4th International Meeting on Applied Sciences and Engineering, EISI 2018 - San Jose de Cucuta, Colombia
Duration: Sep 3 2018Sep 7 2018

ASJC Scopus subject areas

  • Physics and Astronomy(all)

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