Pattern Recognition Assignment - Fully Custom Simulation

Pattern Recognition Assignment - Fully Custom Simulation Program. The implementation first applies Principal Component Analysis (PCA) for dimensionality reduction on the IRIS dataset, then classifies the reduced-dimensional data using the minimum error method. The compressed package includes MATLAB source code with detailed comments, a self-written report, and the IRIS dataset in .MAT format for program invocation. The program outputs final results to a text file with clear algorithmic implementation explanations.

Various Classifiers for IRIS Dataset Classification

Multiple classifiers implementing unsupervised classification of multi-dimensional sampling points on the IRIS dataset. Classifiers can be adapted based on the number of categories, featuring partial code from pattern recognition assignments with implementation insights for machine learning workflows.

Pattern Recognition Assignment - Comparative Analysis of Classification Methods

Pattern recognition assignment implementing classification using mean sample method, mean distance method, nearest neighbor method, and K-nearest neighbors algorithm with code implementation insights

SVM Classifier

SVM classifier designed for classifying multidimensional sample points, adaptable for different numbers of classes. Implements pattern recognition algorithms with configurable kernel functions and hyperparameter optimization.