This paper describes the techniques used to implement the ICE estimator for a multilayer perceptron model, and reviews the performance of the resulting models. The ICE estimator is implemented in the Apache Spark MultilayerPerceptronClassifier, and shown in cross-validation to outperform the stock MultilayerPerceptronClassifier that uses unadjusted MLE (cross-entropy) loss. The resulting models have identical runtime performance, and similar fitting performance to the stock MLP implementations. Additionally, this approach requires no hyper-parameters, and is therefore viable as a drop-in replacement for cross-entropy optimizing multilayer perceptron classifiers wherever overfitting may be a concern.
Cite
@article{arxiv.2007.06157,
title = {Implementing the ICE Estimator in Multilayer Perceptron Classifiers},
author = {Tyler Ward},
journal= {arXiv preprint arXiv:2007.06157},
year = {2020}
}