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Found 6 papers, 1 papers with code
RODD: Robust Outlier Detection in Data Cubes
We propose a general type of test data and examine all methods in a simulation study.
Using Sequential Statistical Tests for Efficient Hyperparameter Tuning
Hyperparameter tuning is one of the the most time-consuming parts in machine learning.
Random boosting and random^2 forests -- A random tree depth injection approach
A Monte Carlo simulation, in which tree-shaped data sets with different numbers of final partitions are built, suggests that there are several scenarios where \emph{Random Boost} and \emph{Random$^2$ Forest} can improve the prediction performance of conventional hierarchical boosting and random forest approaches.
A First Analysis of Kernels for Kriging-based Optimization in Hierarchical Search Spaces
Many real-world optimization problems require significant resources for objective function evaluations.
mlrMBO: A Modular Framework for Model-Based Optimization of Expensive Black-Box Functions
We present mlrMBO, a flexible and comprehensive R toolbox for model-based optimization (MBO), also known as Bayesian optimization, which addresses the problem of expensive black-box optimization by approximating the given objective function through a surrogate regression model.
Fast model selection by limiting SVM training times
Kernelized Support Vector Machines (SVMs) are among the best performing supervised learning methods.
