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Use of Meta-learning to improve deep learning algorithms in classification problems

Grant number: 15/03986-0
Support type:Scholarships abroad - Research Internship - Doctorate
Effective date (Start): October 01, 2015
Effective date (End): September 30, 2016
Field of knowledge:Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
Principal researcher:André Carlos Ponce de Leon Ferreira de Carvalho
Grantee:Rafael Gomes Mantovani
Supervisor abroad: Joaquin Vanschoren
Home Institution: Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil
Research place: Eindhoven University of Technology (TU/e), Netherlands  
Associated to the scholarship:12/23114-9 - Use of meta-learning for parameter tuning for classification problems, BP.DR

Abstract

Machine learning approaches have been widely effective solving many simple and well de- fined problems in the literature. However, most approaches face difficulties when dealing with more complicated real-world problems, often due to high data dimensionality. One of the first applications of Deep Learning models was with data dimensionality reduction. These learning models have attracted a substantial amount of academic and industrial attention in the past years, and are the current state-of-the-art in many applications such as speech recognition, image pedestrian detection, image classification as well as many medical applications. Although building and training deep learning models is highly de- sirable, there lies a big difficulty in selecting the correct parameter settings that needs to be better understood. In this project, we aim to investigate how to combine meta-learning with optimization techniques to efficiently build Deep Learning models for classification tasks. (AU)

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Scientific publications
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
MANTOVANI, RAFAEL G.; ROSSI, ANDRE L. D.; ALCOBACA, EDESIO; VANSCHOREN, JOAQUIN; DE CARVALHO, ANDRE C. P. L. F. A meta-learning recommender system for hyperparameter tuning: Predicting when tuning improves SVM classifiers. INFORMATION SCIENCES, v. 501, p. 193-221, OCT 2019. Web of Science Citations: 0.

Please report errors in scientific publications list by writing to: cdi@fapesp.br.