Bayesian Classifier for Pattern Recognition Implemented in MATLAB

The article discusses how to implement a Bayesian classifier using MATLAB for pattern recognition applications. The Bayesian classifier is a fundamental machine learning algorithm that performs classification tasks based on given datasets. Implementing this classifier in MATLAB enables better understanding and practical application of the algorithm. During the development process, programmers can leverage MATLAB's built-in functions and tools to streamline and accelerate implementation. Key MATLAB functions commonly used include fitcnb for creating naive Bayes models, predict for classification predictions, and probability distribution functions for handling feature likelihoods. The implementation typically involves calculating prior probabilities from training data, estimating class-conditional probabilities using appropriate distributions (Gaussian, multinomial, or kernel), and applying Bayes' theorem for posterior probability computation. Through this implementation, we can classify given data samples to achieve pattern recognition objectives. The development process also allows for parameter tuning and optimization of the Bayesian classifier, such as adjusting smoothing parameters for probability estimates or selecting optimal feature distributions, to enhance classification accuracy and overall performance. Consequently, implementing the Bayesian classifier in MATLAB provides greater flexibility and customizability for our pattern recognition tasks, supporting both parametric and non-parametric approaches based on data characteristics.