C4.5: programs for machine learning
C4.5: programs for machine learning
Surveys in combinatorics, 1993
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
Technical Note: Naive Bayes for Regression
Machine Learning
Learnability of Augmented Naive Bayes in Nonimal Domains
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Geometric implications of the naive Bayes assumption
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
| Hi-index | 0.02 |
It is well known that the naive Bayesian classifier is linear in binary domains. However, little work is done on the learnability of the naive Bayesian classifier in nominal domains, a general case of binary domains. This paper explores the geometric properties of the naive Bayesian classifier in nominal domains. First we propose a three-layer measure for the linearity of functions in nominal domains: hard linear, soft nonlinear, and hard nonlinear. We examine the learnability of the naive Bayesian classifier in terms of that linearity measure.We show that the naive Bayesian classifier can learn some hard linear and some soft nonlinear nominal functions, but still cannot learn any hard nonlinear functions.