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

Research output: Chapter in Book/Report/Conference proceedingConference paper

1 Citation (Scopus)

Abstract

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.
Original languageEnglish
Title of host publicationSSPR 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.
PublisherThe Joint IAPR Intl. Workshops on Syntactical and Structural Pattern Recognition (SSPR 2000) and Statistical Pattern Recognition (SPR 2000), pp. 501-510, Alicante, Spain.
Pages501-510
Number of pages10
Publication statusPublished - 30 Aug 2000
EventUnknown -
Duration: 1 Jan 2000 → …

Conference

ConferenceUnknown
Period1/01/00 → …

Bibliographical note

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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