Data probes, vertical trajectories and classification: a tentative study

David Pearson

International Journal of Applied Mathematics and Computer Science (2007)

  • Volume: 17, Issue: 1, page 107-112
  • ISSN: 1641-876X

Abstract

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In this paper we introduce a method of classification based on data probes. Data points are considered as point masses in space and a probe is simply a particle that is launched into the space. As the probe passes by data clusters, its trajectory will be influenced by the point masses. We use this information to help us to find vertical trajectories. These are trajectories in the input space that are mapped onto the same value in the output space and correspond to the data classes.

How to cite

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Pearson, David. "Data probes, vertical trajectories and classification: a tentative study." International Journal of Applied Mathematics and Computer Science 17.1 (2007): 107-112. <http://eudml.org/doc/207814>.

@article{Pearson2007,
abstract = {In this paper we introduce a method of classification based on data probes. Data points are considered as point masses in space and a probe is simply a particle that is launched into the space. As the probe passes by data clusters, its trajectory will be influenced by the point masses. We use this information to help us to find vertical trajectories. These are trajectories in the input space that are mapped onto the same value in the output space and correspond to the data classes.},
author = {Pearson, David},
journal = {International Journal of Applied Mathematics and Computer Science},
keywords = {output zeroing; classification; nonlinear control; differential geometry},
language = {eng},
number = {1},
pages = {107-112},
title = {Data probes, vertical trajectories and classification: a tentative study},
url = {http://eudml.org/doc/207814},
volume = {17},
year = {2007},
}

TY - JOUR
AU - Pearson, David
TI - Data probes, vertical trajectories and classification: a tentative study
JO - International Journal of Applied Mathematics and Computer Science
PY - 2007
VL - 17
IS - 1
SP - 107
EP - 112
AB - In this paper we introduce a method of classification based on data probes. Data points are considered as point masses in space and a probe is simply a particle that is launched into the space. As the probe passes by data clusters, its trajectory will be influenced by the point masses. We use this information to help us to find vertical trajectories. These are trajectories in the input space that are mapped onto the same value in the output space and correspond to the data classes.
LA - eng
KW - output zeroing; classification; nonlinear control; differential geometry
UR - http://eudml.org/doc/207814
ER -

References

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  3. Hand D.J., Li H.G. and Adams H.G. (2001): Supervised classification with structured class definitions. - Comput. Stat. Data Anal., Vol.36, No.2, pp.209-225. Zbl1080.62526
  4. Hermann R. (1964): Cartan connections and the equivalence problem for geometric structures. - Contrib. Diff. Eqns., Vol.III, No.2, pp.199-248. 
  5. Hermann T. and Ritter H. (1999): Listen to your Data: Model-Based Sonification for Data Analysis, In: Advances in Intelligent Computing and Mulimedia Systems, (M.R. Syed, Edi.), International Institute for Advanced Studies in System Research and Cybernetics, pp.189-194. 
  6. Isidori A. (1995): Nonlinear Control Systems 3rd Ed.. - London: Springer. 
  7. Pearson D.W.(1996): Approximating vertical vector fields for feed forward neural networks. - Appl. Math. Lett., Vol.9, No.2, pp.61-64. Zbl0850.93172
  8. Pearson D.W. and Batton-Hubert M. (2005): Increasing confidence in atmospheric pollution forecasting via vertical vector fields. - Journal Européen des Systèmes Automatisés, Vol.39, No.4, pp.553-569 

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