Zobrazeno 1 - 10
of 43
pro vyhledávání: '"Large, James"'
Publikováno v:
ECML PKDD 2020: Machine Learning and Knowledge Discovery in Databases, pages 660-676, 2020
Using bag of words representations of time series is a popular approach to time series classification. These algorithms involve approximating and discretising windows over a series to form words, then forming a count of words over a given dictionary.
Externí odkaz:
http://arxiv.org/abs/2105.03841
Autor:
Middlehurst, Matthew, Large, James, Flynn, Michael, Lines, Jason, Bostrom, Aaron, Bagnall, Anthony
The Hierarchical Vote Collective of Transformation-based Ensembles (HIVE-COTE) is a heterogeneous meta ensemble for time series classification. HIVE-COTE forms its ensemble from classifiers of multiple domains, including phase-independent shapelets,
Externí odkaz:
http://arxiv.org/abs/2104.07551
Publikováno v:
In proceedings of the IEEE International Conference on Big Data (Big Data), pages 188-195, 2020
Time series classification (TSC) is home to a number of algorithm groups that utilise different kinds of discriminatory patterns. One of these groups describes classifiers that predict using phase dependant intervals. The time series forest (TSF) cla
Externí odkaz:
http://arxiv.org/abs/2008.09172
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-automation of this procedure offers the potential to increase security
Externí odkaz:
http://arxiv.org/abs/2005.02163
Publikováno v:
On the Usage and Performance of the Hierarchical Vote Collective of Transformation-Based Ensembles Version 1.0 (HIVE-COTE v1.0), Lecture Notes in Computer Science book series (LNAI,volume 12588), 2000
The Hierarchical Vote Collective of Transformation-based Ensembles (HIVE-COTE) is a heterogeneous meta ensemble for time series classification. Since it was first proposed in 2016, the algorithm has undergone some minor changes and there is now a con
Externí odkaz:
http://arxiv.org/abs/2004.06069
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 a series, the subseries is shortened and discretised to form a word, t
Externí odkaz:
http://arxiv.org/abs/1911.12008
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 filter, Savitzky-Golay filter, Fourier approximation and a recursive med
Externí odkaz:
http://arxiv.org/abs/1811.00894
Autor:
Bagnall, Anthony, Dau, Hoang Anh, Lines, Jason, Flynn, Michael, Large, James, Bostrom, Aaron, Southam, Paul, Keogh, Eamonn
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 datasets. In October 2018 more datasets were added, bringing the total to
Externí odkaz:
http://arxiv.org/abs/1811.00075
A family of algorithms for time series classification (TSC) involve running a sliding window across each series, discretising the window to form a word, forming a histogram of word counts over the dictionary, then constructing a classifier on the his
Externí odkaz:
http://arxiv.org/abs/1809.06751
Publikováno v:
Data Min Knowl Disc 33, 1674-1709 (2019)
Building classification models is an intrinsically practical exercise that requires many design decisions prior to deployment. We aim to provide some guidance in this decision making process. Specifically, given a classification problem with real val
Externí odkaz:
http://arxiv.org/abs/1710.09220