The Classic Bayes Classifier: Implementation and Applications

This document introduces the classic Bayes classifier, designed to minimize classification errors through statistical learning methods. The classifier operates on Bayesian theorem with conditional independence assumptions between features, applicable for both classification and regression analysis. The MATLAB implementation includes key functions for probability density estimation (using normpdf for Gaussian distributions) and posterior probability calculation through Bayes' rule. For those unfamiliar with MATLAB, the provided examples demonstrate dataset preprocessing, feature normalization, and classifier training with empirical risk minimization. The code structure highlights: 1) Prior probability computation from training data 2) Likelihood estimation using parametric/non-parametric methods 3) Decision-making through argmax of posterior probabilities. Additionally, the document explores application domains like spam filtering and medical diagnosis, comparative analysis with SVM and decision trees, and future research directions including kernel density estimation and online learning adaptations. This resource aims to facilitate practical understanding of Bayes classifiers for real-world implementations.