Pattern Recognition Major Assignment: Parzen Window Implementation and Analysis

This significant pattern recognition assignment provides an in-depth exploration of Parzen window methodology, featuring both theoretical foundations and practical implementation. The project includes detailed mathematical formulations of the Parzen window algorithm, complete with fully functional source code that demonstrates kernel function implementation, bandwidth parameter optimization, and probability density estimation techniques. The implementation utilizes Gaussian kernel functions with customizable smoothing parameters, incorporating efficient data structures for high-dimensional pattern classification. Comprehensive experimental analysis examines classification accuracy, computational efficiency, and parameter sensitivity through rigorous performance metrics. The assignment serves as an excellent resource for academic research and professional development in statistical pattern recognition, featuring practical examples of probability density estimation, decision boundary analysis, and multidimensional feature space processing using non-parametric estimation methods.