Models and algorithm for never-ending learning

• Grant number: 13/07787-6
• Support Opportunities: Regular Research Grants
• Start date: July 01, 2013
• End date: June 30, 2015
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
• Principal Investigator: Estevam Rafael Hruschka Júnior
• Grantee: Estevam Rafael Hruschka Júnior
• Host Institution: Centro de Ciências Exatas e de Tecnologia (CCET). Universidade Federal de São Carlos (UFSCAR). São Carlos , SP, Brazil
• Associated researchers: Thomas Michael Mitchell

Abstract

Machine Learning research has evolved and achieved important results in the last decades. We still don't have, however, a machine learning system capable of performing an autonomous and continuous learning, that can used the knowledge acquired yesterday to enhance its ability to learn better today. The first (and, to our knowledge, the only) never-ending learning system is named NELL (Never-Ending Language Learner). NELL has been developed at Carnegie Mellon University (CMU) in a joint effort with Federal University of Sao Carlos (UFSCar). The main goal of the research work proposed in this project is to keep on the investigation of the never-ending learning topic, and in addition, to propose and implement methods and algorithms to be coupled to never-ending learning systems. (AU)

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)

DO AMARAL, LAURENCE RODRIGUES; DA SILVA ALVES, ALEXANDRE HENRICK; MENDES, RAPHAEL DE LIMA; GOMES, MATHEUS DE SOUZA; LIMA BERTARINI, PEDRO LUIZ; HRUSCHKA JR, ESTEVAM RAFAEL; IEEE. Applying Never-Ending Learning (NEL) Principles to Build a Gene Ontology (GO) Biocurator. 2021 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC 2021), v. N/A, p. 8-pg., 2021-01-01. (13/07787-6)

DA SILVA, NADIA F. F.; HRUSCHKA, EDUARDO R.; HRUSCHKA, JR., ESTEVAM R.. Tweet sentiment analysis with classifier ensembles. DECISION SUPPORT SYSTEMS, v. 66, p. 170-179, OCT 2014. (13/07375-0, 13/07787-6)

DO AMARAL, LAURENCE RODRIGUES; HRUSCHKA, JR., ESTEVAM RAFAEL. Transgenic: An evolutionary algorithm operator. Neurocomputing, v. 127, p. 104-113, MAR 15 2014. (13/07787-6)