1 citations · 1 across the 1 of their papers we have counts for
6 papers
Minimal Learning Machine for Multi-Label Learning
Joonas Hämäläinen, Antoine Hubermont, Amauri Souza +3
Distance-based supervised method, the minimal learning machine, constructs a predictive model from data by learning a mapping between input and output distance matrices. In this pa…
Anomaly Detection in Trajectory Data with Normalizing Flows
Madson L. D. Dias, César Lincoln C. Mattos, Ticiana L. C. da Silva +2
The task of detecting anomalous data patterns is as important in practical applications as challenging. In the context of spatial data, recognition of unexpected trajectories bring…
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…