GUI de MATLAB para la evaluación del potencial eólico en sistemas operativos basados en GNU/Linux
Frequent approximate subgraph mining has emerged as an important research topic where graphs are used for modeling entities and their relations including some distortions in the data. In the last years, there has been a considerable growth in the application of this kind of mining on image classific...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | Spanish |
| Published: |
Universidad de Ciencias Informáticas
2015-01-01
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| Series: | Revista Cubana de Ciencias Informáticas |
| Subjects: | |
| Online Access: | https://rcci.uci.cu/index.php?journal=rcci&page=article&op=view&path[]=897&path[]=305 |
| Summary: | Frequent approximate subgraph mining has emerged as an important research topic where graphs are used for modeling entities and their relations including some distortions in the data. In the last years, there has been a considerable growth in the application of this kind of mining on image classification; achieving competitive results against other approaches. In this nectar, a review of recent contributions on image classification based on frequent approximate subgraph mining is presented. We highlight the usefulness of this type of mining, as well as the improvements achieved in terms of efficiency and efficacy of the proposed frameworks. |
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| ISSN: | 1994-1536 2227-1899 |