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pro vyhledávání: '"McLachlan, Geoffrey J"'
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 gmmsslm, an R package for estimating the Bayes' classifier from such partial
Externí odkaz:
http://arxiv.org/abs/2302.13206
We consider the statistical analysis of heterogeneous data for prediction in situations where the observations include functions, typically time series. We extend the modeling with Mixtures-of-Experts (ME), as a framework of choice in modeling hetero
Externí odkaz:
http://arxiv.org/abs/2202.02249
Autor:
Ahfock, Daniel, McLachlan, Geoffrey J.
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 consists of a limited number of classified observations but a much lar
Externí odkaz:
http://arxiv.org/abs/2104.04046
Data-fusion involves the integration of multiple related datasets. The statistical file-matching problem is a canonical data-fusion problem in multivariate analysis, where the objective is to characterise the joint distribution of a set of variables
Externí odkaz:
http://arxiv.org/abs/2104.02888
Autor:
Ahfock, Daniel, McLachlan, Geoffrey J.
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 ground-truth labels, and label noise is introduced into the training da
Externí odkaz:
http://arxiv.org/abs/2104.02872
Autor:
Koh, Edwin J.Y., Amini, Eiman, Spier, Carlos A., McLachlan, Geoffrey J., Xie, Weiguo, Beaton, Nick
Publikováno v:
In Minerals Engineering January 2024 205
