Conditional maximum likelihood estimation of naive bayes probability models

摘要

A statistical classifier is constructed by estimating Naïve Bayes classifiers such that the conditional likelihood of class given word sequence is maximized. The classifier is constructed using a rational function growth transform implemented for Naïve Bayes classifiers. The estimation method tunes the model parameters jointly for all classes such that the classifier discriminates between the correct class and the incorrect ones for a given training sentence or utterance. Optional parameter smoothing and/or convergence speedup can be used to improve model performance. The classifier can be integrated into a speech utterance classification system or other natural language processing system.