Relatively Simple Pattern Recognition Problem
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Resource Overview
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.
Detailed Documentation
This is a relatively simple pattern recognition problem. We will utilize the data from female.txt and male.txt as our training sample set to construct a Bayes classifier. The implementation typically involves calculating prior probabilities from the training data and computing likelihood functions for each feature dimension. The classifier will then be tested using sample datasets set1.txt, set2.txt, and set3.txt. We will conduct experiments using both individual features and feature pairs to investigate different approaches and their effectiveness. For code implementation, key steps include data preprocessing, probability density estimation using Gaussian distributions, and decision boundary calculation based on maximum a posteriori probability. Feature pairing experiments will require covariance matrix computation for multivariate Gaussian distributions when handling two-dimensional feature spaces.
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