Performance Evaluation on Several Recognition Algorithms
Identifieur interne : 004733 ( Main/Exploration ); précédent : 004732; suivant : 004734Performance Evaluation on Several Recognition Algorithms
Auteurs : Wan Zhang [Hong Kong] ; Philippe Dosch [France] ; Salvatore Tabbone [France]Source :
English descriptors
Abstract
How to evaluate the existing recognition algorithms under a uniform platform is an interesting research topic. Most of the comparisons among the algorithms only take use of the dataset in one field and the recognition accuracy as the standard, it is difficult to make people select a suitable algorithm in practice. In this paper, we try to show the various aspects of the algorithms based on the performance when they are applied to the datasets from different application fields. At the same time, two new performance evaluation measures, homogeneity and separability, are proposed and implemented for the comparisons. Experimental results help the researchers understand the algorithms better.
Url:
Affiliations:
- France, Hong Kong
- Grand Est, Lorraine (région)
- Nancy
- Institut national polytechnique de Lorraine, Université Nancy 2, Université de Lorraine
Links toward previous steps (curation, corpus...)
- to stream Hal, to step Corpus: 003B47
- to stream Hal, to step Curation: 003B47
- to stream Hal, to step Checkpoint: 003854
- to stream Main, to step Merge: 004881
- to stream Main, to step Curation: 004733
Le document en format XML
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Est</region><region type="old region" nuts="2">Lorraine (région)</region></placeName><orgName type="university">Université Nancy 2</orgName><orgName type="institution" wicri:auto="newGroup">Université de Lorraine</orgName><placeName><settlement type="city">Nancy</settlement><region type="region" nuts="2">Grand Est</region><region type="old region" nuts="2">Lorraine (région)</region></placeName><orgName type="university">Institut national polytechnique de Lorraine</orgName><orgName type="institution" wicri:auto="newGroup">Université de Lorraine</orgName></affiliation></author></analytic></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="mix" xml:lang="en"><term>algorithms comparison</term><term>performance evaluation</term><term>symbol recognition</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">How to evaluate the existing recognition algorithms under a uniform platform is an interesting research topic. Most of the comparisons among the algorithms only take use of the dataset in one field and the recognition accuracy as the standard, it is difficult to make people select a suitable algorithm in practice. In this paper, we try to show the various aspects of the algorithms based on the performance when they are applied to the datasets from different application fields. At the same time, two new performance evaluation measures, homogeneity and separability, are proposed and implemented for the comparisons. Experimental results help the researchers understand the algorithms better.</div></front></TEI><affiliations><list><country><li>France</li><li>Hong Kong</li></country><region><li>Grand Est</li><li>Lorraine (région)</li></region><settlement><li>Nancy</li></settlement><orgName><li>Institut national polytechnique de Lorraine</li><li>Université Nancy 2</li><li>Université de Lorraine</li></orgName></list><tree><country name="Hong Kong"><noRegion><name sortKey="Zhang, Wan" sort="Zhang, Wan" uniqKey="Zhang W" first="Wan" last="Zhang">Wan Zhang</name></noRegion></country><country name="France"><region name="Grand Est"><name sortKey="Dosch, Philippe" sort="Dosch, Philippe" uniqKey="Dosch P" first="Philippe" last="Dosch">Philippe Dosch</name></region><name sortKey="Tabbone, Salvatore" sort="Tabbone, Salvatore" uniqKey="Tabbone S" first="Salvatore" last="Tabbone">Salvatore Tabbone</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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