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Found 4 papers, 2 papers with code
Minimal Learning Machine for Multi-Label Learning
Distance-based supervised method, the minimal learning machine, constructs a predictive model from data by learning a mapping between input and output distance matrices.
Minimal Learning Machine: Theoretical Results and Clustering-Based Reference Point Selection
Specifically, for a small number of reference points, the clustering-based methods outperformed the standard random selection of the original MLM formulation.
No-PASt-BO: Normalized Portfolio Allocation Strategy for Bayesian Optimization
Among the main parts of a BO algorithm, the acquisition function is of fundamental importance, since it guides the optimization algorithm by translating the uncertainty of the regression model in a utility measure for each point to be evaluated.
LS-SVR as a Bayesian RBF network
We show theoretical similarities between the Least Squares Support Vector Regression (LS-SVR) model with a Radial Basis Functions (RBF) kernel and maximum a posteriori (MAP) inference on Bayesian RBF networks with a specific Gaussian prior on the regression weights.
