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arXiv · 2007.06157

Implementing the ICE Estimator in Multilayer Perceptron Classifiers

Tyler Ward
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Abstract

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.

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Tyler Ward. 2020-07-13. Implementing the ICE Estimator in Multilayer Perceptron Classifiers. https://arxiv.org/abs/2007.06157

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