4 citations · 4 across the 2 of their papers we have counts for
2 papers
cs.LG2017
Classification and clustering for observations of event time data using non-homogeneous Poisson process models
Duncan Barrack, Simon Preston
Data of the form of event times arise in various applications. A simple model for such data is a non-homogeneous Poisson process (NHPP) which is specified by a rate function that d…
cs.LG2015★ 4 cited
AMP: a new time-frequency feature extraction method for intermittent time-series data
Duncan Barrack, James Goulding, Keith Hopcraft +2
The characterisation of time-series data via their most salient features is extremely important in a range of machine learning task, not least of all with regards to classification…