Deep learning architecture for the recursive patterns recognition model

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

Research output: Contribution to journalConference article

2 Citations (Scopus)

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

Fingerprint

pattern recognition
learning
hierarchies
abbreviations
machine learning

ASJC Scopus subject areas

  • Physics and Astronomy(all)

Cite this

Deep learning architecture for the recursive patterns recognition model. / Puerto, E.; Aguilar, J.; Reyes, J.; Sarkar, D.

In: Journal of Physics: Conference Series, Vol. 1126, No. 1, 012035, 07.12.2018.

Research output: Contribution to journalConference article

@article{d3b2013cbbe94f378f88b4a57b48c668,
title = "Deep learning architecture for the recursive patterns recognition model",
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.",
author = "E. Puerto and J. Aguilar and J. Reyes and D. Sarkar",
year = "2018",
month = "12",
day = "7",
doi = "10.1088/1742-6596/1126/1/012035",
language = "English (US)",
volume = "1126",
journal = "Journal of Physics: Conference Series",
issn = "1742-6588",
publisher = "IOP Publishing Ltd.",
number = "1",

}

TY - JOUR

T1 - Deep learning architecture for the recursive patterns recognition model

AU - Puerto, E.

AU - Aguilar, J.

AU - Reyes, J.

AU - Sarkar, D.

PY - 2018/12/7

Y1 - 2018/12/7

N2 - 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.

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.

UR - http://www.scopus.com/inward/record.url?scp=85058656811&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85058656811&partnerID=8YFLogxK

U2 - 10.1088/1742-6596/1126/1/012035

DO - 10.1088/1742-6596/1126/1/012035

M3 - Conference article

AN - SCOPUS:85058656811

VL - 1126

JO - Journal of Physics: Conference Series

JF - Journal of Physics: Conference Series

SN - 1742-6588

IS - 1

M1 - 012035

ER -