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首页> 外文会议>International Conference on Computer Vision Theory and Applications >Comparison of Statistical and Artificial Neural Networks Classifiers by Adjusted Non Parametric Probability Density Function Estimate
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Comparison of Statistical and Artificial Neural Networks Classifiers by Adjusted Non Parametric Probability Density Function Estimate

机译：通过调整后的非参数概率密度函数估计的统计和人工神经网络分类器的比较

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摘要

In the industrial field, the artificial neural network classifiers are currently used and they are generally integrated of technologic systems which need efficient classifier. Statistical classifiers also have been developed in the same direction and different associations and optimization procedures have been proposed as Adaboost training or CART algorithm to improve the classification performance. However, the objective comparison studies between these novel classifiers stay marginal. In the present work, we intend to evaluate with a new criterion the classification stability between neural networks and some statistical classifiers based on the optimization Fischer criterion or the maximization of Patrick-Fischer distance orthogonal estimator. The stability comparison is performed by the error rate probability densities estimation which is valorised by the performed kernel-diffeomorphism Plug-in algorithm. The results obtained show that the statistical approaches are more stable compared to the neural networks.
机译：在工业领域，目前使用人工神经网络分类器，它们通常是需要高效分类器的技术系统。统计分类器也已经在同一方向和不同的关联和优化程序中开发，已提出作为Adaboost训练或购物车算法来提高分类性能。然而，这些新颖分类器之间的客观比较研究保持边缘。在本作工作中，我们打算根据优化费 - 距离标准或帕特里克 - 费解人距离正交估计器的最大化来评估神经网络与一些统计分类器之间的分类稳定性。稳定性比较是由所执行的内核 - 漫射术插件算法估值的误差率概率密度估计来执行。得到的结果表明，与神经网络相比，统计方法更稳定。

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