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Investigation and evaluation of rank correlation measures

Grant number: 21/07993-1
Support Opportunities:Scholarships in Brazil - Scientific Initiation
Effective date (Start): September 01, 2021
Effective date (End): August 31, 2022
Field of knowledge:Physical Sciences and Mathematics - Computer Science
Principal Investigator:Daniel Carlos Guimarães Pedronette
Grantee:Vinicius Atsushi Sato Kawai
Host Institution: Instituto de Geociências e Ciências Exatas (IGCE). Universidade Estadual Paulista (UNESP). Campus de Rio Claro. Rio Claro , SP, Brazil
Associated research grant:18/15597-6 - Aplication and investigation of unsupervised learning methods in retrieval and classification tasks, AP.JP2

Abstract

Similarity information based on contextual analysis has great potential for various machine learning and information retrieval tasks. Rank correlation measures provide an effective tool for contextual similarity analysis. Recently, these measures have been successfully exploited in unsupervised and weakly supervised domains. In this scenario, the project aims to evaluate and investigate novel measures. The main idea consists of evaluating existing measures and investigating the development of metrics that can increase the effectiveness of the methods in which they are used. (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)
VALEM, LUCAS PASCOTTI; SATO KAWAI, VINICIUS ATSUSHI; PEREIRA-FERRERO, VANESSA HELENA; GUIMARAES PEDRONETTE, DANIEL CARLOS; IEEE. A NOVEL RANK CORRELATION MEASURE FOR MANIFOLD LEARNING ON IMAGE RETRIEVAL AND PERSON RE-ID. 2022 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, ICIP, v. N/A, p. 5-pg., . (18/15597-6, 20/02183-9, 21/07993-1, 20/11366-0)

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