192 citations · 426 across the 48 of their papers we have counts for
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Analysis of Estimating the Bayes Rule for Gaussian Mixture Models with a Specified Missing-Data Mechanism
Ziyang Lyu
Semi-supervised learning (SSL) approaches have been successfully applied in a wide range of engineering and scientific fields. This paper investigates the generative model framewor…
Harmless label noise and informative soft-labels in supervised classification
Daniel Ahfock, Geoffrey J. McLachlan
Manual labelling of training examples is common practice in supervised learning. When the labelling task is of non-trivial difficulty, the supplied labels may not be equal to the g…
Semi-Supervised Learning of Classifiers from a Statistical Perspective: A Brief Review
Daniel Ahfock, Geoffrey J. McLachlan
There has been increasing attention to semi-supervised learning (SSL) approaches in machine learning to forming a classifier in situations where the training data for a classifier…
Estimation of Classification Rules from Partially Classified Data
Geoffrey J. McLachlan, Daniel Ahfock
We consider the situation where the observed sample contains some observations whose class of origin is known (that is, they are classified with respect to the g underlying classes…
Deep Gaussian Mixture Models
Cinzia Viroli, Geoffrey J. McLachlan
Deep learning is a hierarchical inference method formed by subsequent multiple layers of learning able to more efficiently describe complex relationships. In this work, Deep Gaussi…
A Universal Approximation Theorem for Mixture of Experts Models
Hien D Nguyen, Luke R Lloyd-Jones, Geoffrey J McLachlan
The mixture of experts (MoE) model is a popular neural network architecture for nonlinear regression and classification. The class of MoE mean functions is known to be uniformly co…