Class Mean Vectors, Self Monitoring and Self Learning for Neural Classifiers
Machine Learning
2019-10-23 v1 Machine Learning
Abstract
In this paper we explore the role of sample mean in building a neural network for classification. This role is surprisingly extensive and includes: direct computation of weights without training, performance monitoring for samples without known classification, and self-training for unlabeled data. Experimental computation on a CIFAR-10 data set provides promising empirical evidence on the efficacy of a simple and widely applicable approach to some difficult problems.
Keywords
Cite
@article{arxiv.1910.10122,
title = {Class Mean Vectors, Self Monitoring and Self Learning for Neural Classifiers},
author = {Eugene Wong},
journal= {arXiv preprint arXiv:1910.10122},
year = {2019}
}