Probability Density Function Estimation Using Parzen Window Method in Pattern Recognition

This project demonstrates Parzen window method for probability density function estimation in pattern recognition. The complete program workflow includes: 1) Reading height/weight data from FAMALE.TXT into arrays, calculating sample size N1 and window parameters; 2) Processing MALE.TXT similarly to obtain N2 and parameters; 3) Loading test data from Test2.txt into array A with sample size M; 4) Implementing Parzen window density estimation for gender classification; 5) Statistical analysis of classification error rates. The implementation involves kernel function selection and bandwidth optimization for non-parametric density estimation.