Lerner B, Guterman H, Dinstein I, Romem Y
Department of Electrical and Computer Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
Int J Neural Syst. 1995 Sep;6(3):359-70. doi: 10.1142/s012906579500024x.
A multilayer perceptron (MLP) neural network (NN) has been studied for human chromosome classification. Only 10-20 examples were required for the MLP NN to reach its ultimate performance classifying chromosomes of 5 types. The empirical dependence of the entropic error on the number of examples was found to be highly comparable to the 1/t function. The principal component analysis (PCA) was used, both for network initialization and for feature reduction purposes. The PCA demonstrated the importance of retaining most of the image information whenever small training sets are used. The MLP NN classifier outperformed the Bayes piecewise classifier for all the cases tested. The MLP classifier was found to be almost unsusceptible to the ratio of the number of training vectors to the number of features, whereas the piecewise classifier was highly dependent on this ratio.
多层感知器（MLP）神经网络已被用于人类染色体分类研究。MLP神经网络仅需10至20个示例就能达到对5种类型染色体进行分类的最终性能。发现熵误差与示例数量之间的经验依赖关系与1/t函数高度可比。主成分分析（PCA）用于网络初始化和特征约简。PCA表明，在使用小训练集时保留大部分图像信息的重要性。在所有测试案例中，MLP神经网络分类器的性能均优于贝叶斯分段分类器。发现MLP分类器几乎不受训练向量数量与特征数量之比的影响，而分段分类器则高度依赖于此比例。
