Bayes Error Estimation Using Parzen and k-NN Procedures
Bayes Error Estimation Using Parzen and k-NN Procedures
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DOI：
10.1109/tpami.1987.4767958
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发表时间：
1987-05
影响因子：
23.6
通讯作者：
K. Fukunaga;D. Hummels
中科院分区：
文献类型：
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作者：
K. Fukunaga;D. Hummels
研究了在有限设计集条件下，利用k近邻(k-NN)估计和Parzen密度估计来获得Bayes误差的估计。通过对k-NN和Parzen方法的类比，提出了新的方法，并给出了实验结果，实验结果表明，这些方法比传统的k-NN和Parzen方法有显著的改进。我们表明，通过改变判决阈值，可以补偿与k-NN或Parzen密度估计相关的许多偏差，并且尽管存在这些偏差，仍可以执行成功的误差估计。实验结果表明，核的大小和形状(Parzen)、k的大小(k-NN)和设计集合中的样本数量都会对分类结果产生影响。
The use of k nearest neighbor (k-NN) and Parzen density estimates to obtain estimates of the Bayes error is investigated under limited design set conditions. By drawing analogies between the k-NN and Parzen procedures, new procedures are suggested, and experimental results are given which indicate that these procedures yield a significant improvement over the conventional k-NN and Parzen procedures. We show that, by varying the decision threshold, many of the biases associated with the k-NN or Parzen density estimates may be compensated, and successful error estimation may be performed in spite of these biases. Experimental results are given which demonstrate the effect of kernel size and shape (Parzen), the size of k (k-NN), and the number of samples in the design set.