Performance Evaluation on Several Recognition Algorithms
Résumé
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.