论文标题
本地近似，实际插值和机器学习
Local Approximations, Real Interpolation and Machine Learning
论文作者
论文摘要
我们建议一种基于局部近似值的新型分类算法，并解释其与人工神经网络（ANN）和最近的邻居分类器的联系。我们在数据集MNIST和手写数字图像的Emnist上进行了说明。我们使用数据集MNIST查找算法的参数，并将这些参数应用于具有挑战性的EMNIST数据集。证明该算法错误分类为0.42％的Emnist图像，因此明显优于人类和浅层人工神经网络（几乎没有隐藏层的ANN）的预测，这两者都有超过1.3％的错误
We suggest a novel classification algorithm that is based on local approximations and explain its connections with Artificial Neural Networks (ANNs) and Nearest Neighbour classifiers. We illustrate it on the datasets MNIST and EMNIST of images of handwritten digits. We use the dataset MNIST to find parameters of our algorithm and apply it with these parameters to the challenging EMNIST dataset. It is demonstrated that the algorithm misclassifies 0.42% of the images of EMNIST and therefore significantly outperforms predictions by humans and shallow artificial neural networks (ANNs with few hidden layers) that both have more than 1.3% of errors
