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Models and algorithm for never-ending learning

Grant number: 13/07787-6
Support type:Regular Research Grants
Duration: July 01, 2013 - 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
Home Institution: Centro de Ciências Exatas e de Tecnologia (CCET). Universidade Federal de São Carlos (UFSCAR). São Carlos , SP, Brazil
Assoc. 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)
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. Web of Science Citations: 103.
DO AMARAL, LAURENCE RODRIGUES; HRUSCHKA, JR., ESTEVAM RAFAEL. Transgenic: An evolutionary algorithm operator. Neurocomputing, v. 127, p. 104-113, MAR 15 2014. Web of Science Citations: 1.

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