Building Linear Discriminant Functions Using Minimum Squared Error Criterion (MSE Criterion) Through Training Sample Sets

This paper applies the Minimum Squared Error Criterion (MSE Criterion) to construct linear discriminant functions from training datasets and utilizes these functions for test set classification. The implementation uses three feature datasets: 1) Gender data (male/female), 2) SONA academic metrics, and 3) UPS performance scores, with Python/numpy implementations for matrix operations and weight optimization.

MATLAB Implementation of PCA Face Recognition with Complete Code Solution

This PCA face recognition program includes trained face dataset and demonstrates efficient implementation of Principal Component Analysis for face recognition. The code runs smoothly in MATLAB environment and provides high recognition accuracy through optimized feature extraction algorithms.

Relatively Simple Pattern Recognition Problem

This project implements a straightforward pattern recognition task using Bayes classifier. The training dataset consists of female.txt and male.txt files, while the classifier performance is evaluated using test datasets set1.txt, set2.txt, and set3.txt. The implementation involves experiments with both single features and feature pairs to analyze classification accuracy.