Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
The fast Gauss transform with variable scales
SIAM Journal on Scientific and Statistical Computing
Efficient Locally Weighted Polynomial Regression Predictions
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Fast Computation of Tagged MRI Motion Fields with Subspace Approximation Techniques
MMBIA '00 Proceedings of the IEEE Workshop on Mathematical Methods in Biomedical Image Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Classification of flourescence in situ hybridization images using belief networks
Pattern Recognition Letters
B-spline active contour with handling of topology changes for fast video segmentation
EURASIP Journal on Applied Signal Processing
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
A fast efficient computation of cubic-spline interpolation in imagecodec
IEEE Transactions on Signal Processing
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Bayesian fluorescence in situ hybridisation signal classification
Artificial Intelligence in Medicine
Bayesian classifiers based on kernel density estimation: Flexible classifiers
International Journal of Approximate Reasoning
On the properties of concept classes induced by multivalued Bayesian networks
Information Sciences: an International Journal
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The likelihood for patterns of continuous features needed for probabilistic inference in a Bayesian network classifier (BNC) may be computed by kernel density estimation (KDE), letting every pattern influence the shape of the probability density. Although usually leading to accurate estimation, the KDE suffers from computational cost making it unpractical in many real-world applications. We smooth the density using a spline thus requiring for the estimation only very few coefficients rather than the whole training set allowing rapid implementation of the BNC without sacrificing classifier accuracy. Experiments conducted over a several real-world databases reveal acceleration in computational speed, sometimes in several orders of magnitude, in favor of our method making the application of KDE to BNCs practical. al.