Local Approximations, Real Interpolation and Machine Learning
This work addresses classification accuracy in machine learning, specifically for image recognition tasks, and is incremental as it builds on existing methods like ANNs and nearest neighbor classifiers.
The paper tackles the problem of image classification for handwritten digits by proposing a novel algorithm based on local approximations, achieving a misclassification rate of 0.42% on the EMNIST dataset.
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