"Pattern Classiffcation Based on a Piecewise Multi-Linear Model for the Class Probability Densities

Onderzoeksoutput: Conference paper

1 Citaat (Scopus)

Samenvatting

When a Bayesian classifier is designed, a model for the class probability density functions (PDFs) has to be chosen. This choice is determined by a trade-off between robustness and low complexity -- which is usually satisfied by simple parametric models, based on a restricted number of parameters -- and the model's ability to fit a large class of PDFs -- which usually requires a high number of model parameters. In this paper, a model is introduced, where the class PDFs are approximated as piecewise multi-linear functions (a generalisation of bilinear functions for an arbitrary dimensionality). This model is compared with classical parametric and non-parametric models, from a point of view of versatility, robustness and complexity. The results of classification and PDF estimation experiments are discussed.
Originele taal-2English
TitelSSPR 2000, SPR 2000, Proc. Joint IAPR Intl. Workshops on Syntactical and Structural Pattern Recog- nition and Statistical Pattern Recognition; Alicante, Spain; August 30 - September 1, 2000.
UitgeverijThe Joint IAPR Intl. Workshops on Syntactical and Structural Pattern Recognition (SSPR 2000) and Statistical Pattern Recognition (SPR 2000), pp. 501-510, Alicante, Spain.
Pagina's501-510
Aantal pagina's10
StatusPublished - 30 aug 2000
EvenementUnknown -
Duur: 1 jan 2000 → …

Conference

ConferenceUnknown
Periode1/01/00 → …

Bibliografische nota

The Joint IAPR Intl. Workshops on Syntactical and Structural Pattern Recognition (SSPR 2000) and Statistical Pattern Recognition (SPR 2000), pp. 501-510, Alicante, Spain.

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