Temporal Classification: Extending the Classification Paradigm to Multivariate Time Series (2002)
by
M.W. Kadous
,
Mohammed Waleed Kadous
,
Supervisor Claude Sammut
| Citations: | 22 - 0 self |
BibTeX
@TECHREPORT{Kadous02temporalclassification:,
author = {M.W. Kadous and Mohammed Waleed Kadous and Supervisor Claude Sammut},
title = {Temporal Classification: Extending the Classification Paradigm to Multivariate Time Series},
institution = {},
year = {2002}
}
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Abstract
Machine learning research has, to a great extent, ignored an important aspect of many real world applications: time. Existing concept learners predominantly operate on a static set of attributes; for example, classifying flowers described by leaf size, petal colour and petal count. The values of these attributes is assumed to be unchanging -- the flower never grows or loses leaves.
