Predicting the Performance of Learning Algorithms Using Support Vector Machines as Meta-regressors

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Hyper-Parameter Optimization: A Review of Algorithms and Applications (2020, methodology): “Guerra made the prediction using support vector machines (Guerra et al., 2008); Klein used Bayesian neural networks to build the surrogate model (Klein et al.”
Automated Machine Learning: State-of-The-Art and Open Challenges (2019, background): “Moreover, they can also be used to predict the performance of new task based on a particular configuration [105, 45].”
Efficient Benchmarking of Hyperparameter Optimizers via Surrogates (2015, methodology): “In the field of meta-learning (Brazdil et al. 2008), regression models have been extensively used to predict the performance of algorithms across various datasets based on dataset features (Guerra, Prudêncio, and Ludermir 2008; Reif et al. 2014).”
Surrogate Benchmarks for Hyperparameter Optimization (2014, methodology): “In the field of metalearning [6], regression models have been extensively used to predict the performance of algorithms across various datasets based on dataset features [11, 26].”
Beyond Manual Tuning of Hyperparameters (2015, methodology): “From this meta-dataset then one can learn any classification or ranking model that will predict the best algorithm to be used for the target dataset/task (rules [2], decision trees [11], SVMs [25], etc.”