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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Choosing the Most Effective Pattern Classification Model under Learning-Time Constraint

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Author(s):
Saito, Priscila T. M. [1] ; Nakamura, Rodrigo Y. M. [2] ; Amorim, Willian P. [3] ; Papa, Joao P. [4] ; de Rezende, Pedro J. [5] ; Falcao, Alexandre X. [5]
Total Authors: 6
Affiliation:
[1] Univ Tecnol Fed Parana, Dept Comp, Cornelio Procopio - Brazil
[2] Big Data Brazil, Sao Paulo, SP - Brazil
[3] Univ Fed Mato Grosso do Sul, Inst Comp, Campo Grande - Brazil
[4] Sao Paulo State Univ, Dept Comp, Bauru - Brazil
[5] Univ Estadual Campinas, Inst Comp, Campinas, SP - Brazil
Total Affiliations: 5
Document type: Journal article
Source: PLoS One; v. 10, n. 6 JUN 26 2015.
Web of Science Citations: 3
Abstract

Nowadays, large datasets are common and demand faster and more effective pattern analysis techniques. However, methodologies to compare classifiers usually do not take into account the learning-time constraints required by applications. This work presents a methodology to compare classifiers with respect to their ability to learn from classification errors on a large learning set, within a given time limit. Faster techniques may acquire more training samples, but only when they are more effective will they achieve higher performance on unseen testing sets. We demonstrate this result using several techniques, multiple datasets, and typical learning-time limits required by applications. (AU)

FAPESP's process: 14/16250-9 - On the parameter optimization in machine learning techniques: advances and paradigms
Grantee:João Paulo Papa
Support Opportunities: Regular Research Grants
FAPESP's process: 11/14058-5 - Exploring Sequential Learning Approaches for Optimum-Path Forest
Grantee:Rodrigo Yuji Mizobe Nakamura
Support Opportunities: Scholarships in Brazil - Master
FAPESP's process: 13/20387-7 - Hyperparameter optimization in deep learning arquitectures
Grantee:João Paulo Papa
Support Opportunities: Scholarships abroad - Research
FAPESP's process: 07/52015-0 - Approximation methods for visual computing
Grantee:Jorge Stolfi
Support Opportunities: Research Projects - Thematic Grants
FAPESP's process: 12/18768-0 - Multiscale Classification By Using Optimum Path-Forest
Grantee:Jefersson A dos Santos
Support Opportunities: Scholarships in Brazil - Post-Doctoral