Some representations of the multivariate Bernoulli and binomial distributions
Journal of Multivariate Analysis
Elements of information theory
Elements of information theory
Probably almost Bayes decisions
Information and Computation
Approximating Bayesian Belief Networks by Arc Removal
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
Machine Learning - Special issue on learning with probabilistic representations
An Introduction to Variational Methods for Graphical Models
Machine Learning
Principles of data mining
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
On Bias, Variance, 0/1—Loss, and the Curse-of-Dimensionality
Data Mining and Knowledge Discovery
Gröbner bases and factorisation in discrete probability and Bayes
Statistics and Computing
Bayesian Artificial Intelligence
Bayesian Artificial Intelligence
Optimal structure identification with greedy search
The Journal of Machine Learning Research
Bayesian Nets And Causality: Philosophical And Computational Foundations
Bayesian Nets And Causality: Philosophical And Computational Foundations
Finite mixture model of bounded semi-naive Bayesian networks classifier
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Context-specific independence in Bayesian networks
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Some inequalities for information divergence and related measures of discrimination
IEEE Transactions on Information Theory
Channel equalization for block transmission systems
IEEE Journal on Selected Areas in Communications
On Concentration of Discrete Distributions with Applications to Supervised Learning of Classifiers
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
The importance of statistical evidence for focussed Bayesian fusion
KI'10 Proceedings of the 33rd annual German conference on Advances in artificial intelligence
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In many pattern recognition/classification problem the true class conditional model and class probabilities are approximated for reasons of reducing complexity and/or of statistical estimation. The approximated classifier is expected to have worse performance, here measured by the probability of correct classification. We present an analysis valid in general, and easily computable formulas for estimating the degradation in probability of correct classification when compared to the optimal classifier. An example of an approximation is the Naïve Bayes classifier. We show that the performance of the Naïve Bayes depends on the degree of functional dependence between the features and labels. We provide a sufficient condition for zero loss of performance, too.