Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/243084
Type: Artigo
Title: Choosing the most effective pattern classification model under learning-time constraint
Author: Saito, Priscila T. M.
Nakamura, Rodrigo Y. M.
Amorim, Willian P.
Papa, João P.
Rezende, Pedro J. de
Falcão, Alexandre X.
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.
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
Subject: Algoritmos
Inteligência artificial
Aprendizado de máquina
Reconhecimento de padrões
Country: Estados Unidos
Editor: Public Library of Science
Citation: Choosing The Most Effective Pattern Classification Model Under Learning-time Constraint. Public Library Science, v. 10, p. Jun-2015.
Rights: aberto
Aberto
Identifier DOI: 10.1371/journal.pone.0129947
Address: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0129947
Date Issue: 2015
Appears in Collections:IC - Artigos e Outros Documentos

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