1 citations · 2 across the 2 of their papers we have counts for
5 papers
Detecting Electric Devices in 3D Images of Bags
Anthony Bagnall, Paul Southam, James Large +1
The aviation and transport security industries face the challenge of screening high volumes of baggage for threats and contraband in the minimum time possible. Automation and semi-…
A tale of two toolkits, report the second: bake off redux. Chapter 1. dictionary based classifiers
Anthony Bagnall, James Large, Matthew Middlehurst
Time series classification (TSC) is the problem of learning labels from time dependent data. One class of algorithms is derived from a bag of words approach. A window is run along…
Can automated smoothing significantly improve benchmark time series classification algorithms?
James Large, Paul Southam, Anthony Bagnall
tl;dr: no, it cannot, at least not on average on the standard archive problems. We assess whether using six smoothing algorithms (moving average, exponential smoothing, Gaussian fi…
The UEA multivariate time series classification archive, 2018
Anthony Bagnall, Hoang Anh Dau, Jason Lines +5
In 2002, the UCR time series classification archive was first released with sixteen datasets. It gradually expanded, until 2015 when it increased in size from 45 datasets to 85 dat…
Is rotation forest the best classifier for problems with continuous features?
A. Bagnall, M. Flynn, J. Large +3
In short, our experiments suggest that yes, on average, rotation forest is better than the most common alternatives when all the attributes are real-valued. Rotation forest is a tr…