Does configuration encoding matter in learning software performance?

Author:

Gong Jingzhi1,Chen Tao1

Affiliation:

1. Loughborough University, Loughborough, UK

Publisher

ACM

Reference56 articles.

1. Binarsity: a penalization for one-hot encoded features in linear supervised learning;Alaya Mokhtar Z.;J. Mach. Learn. Res.,2019

2. ACTGAN: Automatic Configuration Tuning for Software Systems with Generative Adversarial Networks

3. Tianfeng Chai and Roland R Draxler . 2014. Root mean square error (RMSE) or mean absolute error (MAE)?-Arguments against avoiding RMSE in the literature. Geoscientific model development 7, 3 ( 2014 ), 1247--1250. Tianfeng Chai and Roland R Draxler. 2014. Root mean square error (RMSE) or mean absolute error (MAE)?-Arguments against avoiding RMSE in the literature. Geoscientific model development 7, 3 (2014), 1247--1250.

4. All Versus One: An Empirical Comparison on Retrained and Incremental Machine Learning for Modeling Performance of Adaptable Software

5. Lifelong Dynamic Optimization for Self-Adaptive Systems: Fact or Fiction?

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