12 citations · 13 across the 3 of their papers we have counts for
4 papers
Minimal Learning Machine: Theoretical Results and Clustering-Based Reference Point Selection
Joonas Hämäläinen, Alisson S. C. Alencar, Tommi Kärkkäinen +3
The Minimal Learning Machine (MLM) is a nonlinear supervised approach based on learning a linear mapping between distance matrices computed in the input and output data spaces, whe…
No-PASt-BO: Normalized Portfolio Allocation Strategy for Bayesian Optimization
Thiago de P. Vasconcelos, Daniel A. R. M. A. de Souza, César L. C. Mattos +1
Bayesian Optimization (BO) is a framework for black-box optimization that is especially suitable for expensive cost functions. Among the main parts of a BO algorithm, the acquisiti…
Learning GPLVM with arbitrary kernels using the unscented transformation
Daniel Augusto R. M. A. de Souza, Diego Mesquita, César Lincoln C. Mattos +1
Gaussian Process Latent Variable Model (GPLVM) is a flexible framework to handle uncertain inputs in Gaussian Processes (GPs) and incorporate GPs as components of larger graphical…
LS-SVR as a Bayesian RBF network
Diego P. P. Mesquita, Luis A. Freitas, João P. P. Gomes +1
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) infere…