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arXiv · 1909.12226

A Heuristic for Efficient Reduction in Hidden Layer Combinations For Feedforward Neural Networks

Wei Hao Khoong
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Abstract

In this paper, we describe the hyper-parameter search problem in the field of machine learning and present a heuristic approach in an attempt to tackle it. In most learning algorithms, a set of hyper-parameters must be determined before training commences. The choice of hyper-parameters can affect the final model's performance significantly, but yet determining a good choice of hyper-parameters is in most cases complex and consumes large amount of computing resources. In this paper, we show the differences between an exhaustive search of hyper-parameters and a heuristic search, and show that there is a significant reduction in time taken to obtain the resulting model with marginal differences in evaluation metrics when compared to the benchmark case.

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Wei Hao Khoong. 2019-09-25. A Heuristic for Efficient Reduction in Hidden Layer Combinations For Feedforward Neural Networks. https://arxiv.org/abs/1909.12226

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