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Early transition detection - A dynamic extension to common classification methods

delete1998-09-01
delete2
PRE
AI
M
Michael Marth *
D
Daniel Maier
J
Josef Honerkamp
M
M. Rupprecht
J
J. Goschnick
DOI：10.1016/S0169-7439(98)00081-1delete
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Abstract

Abstract

En 中文
An extension for classification methods in order to process time-dependent data is introduced. It is based on the detection of transitions from one steady state to another one by examination of the time derivatives of classification vectors. The method is called Early Transition Detection (ETD). It is shown that it can be used in conjunction with a number of common classification methods like SIMCA or Artificial Neural Nets and it is successfully tested on simulated and on real data. (C) 1998 Elsevier Science B.V. All rights reserved.
Keywords:
classification
time-dependence
transition
early transition detection
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Journal

Chemometrics and Intelligent Laboratory Systems cover
Chemometrics and Intelligent Laboratory Systems
IF:
3.8
Papers:
4.6K
Citations:
1.2W

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