Bayesian discriminant analysis based on MATLAB
2017-11-25
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For a two class classification problem, when n = 100, mvnrnd() function is used to generate two classes of samples randomly; the sample size of each class is not less than 100; 2) maximum likelihood estimation algorithm is designed to estimate the conditional probability density function of two classes; 3) nonparametric estimation algorithm is designed to estimate the conditional probability density function of two classes (one of Parzen window method or kn nearest neighbor method is optional), and the results are divided into two groups This paper analyzes the influence of sample number, window width, K and other factors on the estimation of probability density function; 4) using the class conditional probability density function estimated in 2) and 3) to design the minimum error probability Bayesian classifier to realize the classification of two kinds of samples. Application of Bayesian discriminant in data processing
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