论文标题
改进的最近的邻居分类器
An Improved Nearest Neighbour Classifier
论文作者
论文摘要
描述了用于图像的最近的邻居（WNN）分类器的窗口版本。尽管它的构建灵感来自人工神经网络的架构，但基本的理论框架基于近似理论。我们在数据集MNIST上说明了手写数字图像的Emnist上的WNN。为了校准WNN的参数，我们首先在经典的MNIST数据集上进行研究。然后，我们将其与这些参数一起应用于具有挑战性的EMNIST数据集。已经证明，WNN误解了emnist的0.42％的图像，因此显着优于人类和浅不会预测的，这些预测都超过1.3％的错误。
A windowed version of the Nearest Neighbour (WNN) classifier for images is described. While its construction is inspired by the architecture of Artificial Neural Networks, the underlying theoretical framework is based on approximation theory. We illustrate WNN on the datasets MNIST and EMNIST of images of handwritten digits. In order to calibrate the parameters of WNN, we first study it on the classical MNIST dataset. We then apply WNN with these parameters to the challenging EMNIST dataset. It is demonstrated that WNN misclassifies 0.42% of the images of EMNIST and therefore significantly outperforms predictions by humans and shallow ANNs that both have more than 1.3% of errors.
