Rapid spline-based kernel density estimation for Bayesian networks

Yaniv Gurwicz, Boaz Lerner

Research output: Contribution to journalConference articlepeer-review

6 Scopus citations


The likelihood for patterns of continuous attributes for the naive Bayesian classifier (NBC) may be approximated by kernel density estimation (KDE), letting every pattern influence the shape of the probability density thus leading to accurate estimation. KDE suffers from computational cost making it unpractical in many real-world applications. We smooth the density using a spline thus requiring only very few coefficients for the estimation rather than the whole training set, allowing rapid implementation of the NBC without sacrificing classifier accuracy. Experiments conducted over several real-world databases reveal acceleration, sometimes in several orders of magnitude, in favor of the spline approximation making the application of KDE to the NBC practical.

Original languageEnglish
Pages (from-to)700-703
Number of pages4
JournalProceedings - International Conference on Pattern Recognition
StatePublished - 1 Jan 2004
EventProceedings of the 17th International Conference on Pattern Recognition, ICPR 2004 - Cambridge, United Kingdom
Duration: 23 Aug 200426 Aug 2004

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition


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