192 citations · 426 across the 48 of their papers we have counts for
15 papers · 1 filter
Semi-supervised Gaussian mixture modelling with a missing-data mechanism in R
Ziyang Lyu, Daniel Ahfock, Ryan Thompson +1
Semi-supervised learning is being extensively applied to estimate classifiers from training data in which not all the labels of the feature vectors are available. We present gmmssl…
Multi-Node EM Algorithm for Finite Mixture Models
Sharon X. Lee, Geoffrey J. McLachlan, Kaleb L. Leemaqz
Finite mixture models are powerful tools for modelling and analyzing heterogeneous data. Parameter estimation is typically carried out using maximum likelihood estimation via the E…
Mini-batch learning of exponential family finite mixture models
H D Nguyen, F Forbes, G J McLachlan
Mini-batch algorithms have become increasingly popular due to the requirement for solving optimization problems, based on large-scale data sets. Using an existing online expectatio…
Randomized Mixture Models for Probability Density Approximation and Estimation
Hien D. Nguyen, Dianhui Wang, Geoffrey J. McLachlan
Randomized neural networks (NNs) are an interesting alternative to conventional NNs that are more used for data modeling. The random vector functional-link (RVFL) network is an est…
Positive data kernel density estimation via the logKDE package for R
Andrew T. Jones, Hien D. Nguyen, Geoffrey J. McLachlan
Kernel density estimators (KDEs) are ubiquitous tools for nonparametric estimation of probability density functions (PDFs), when data are obtained from unknown data generating proc…
Iteratively-Reweighted Least-Squares Fitting of Support Vector Machines: A Majorization--Minimization Algorithm Approach
Hien D. Nguyen, Geoffrey J. McLachlan
Support vector machines (SVMs) are an important tool in modern data analysis. Traditionally, support vector machines have been fitted via quadratic programming, either using purpos…