Two-Dimensional Three-Class Sample Classification with Decision Boundary Visualization

This program enables classification of two-dimensional samples into three distinct categories while providing visual representation of decision boundaries. The implementation utilizes fundamental pattern recognition algorithms, potentially employing techniques like linear discriminant analysis or k-nearest neighbors for class separation. For students of pattern recognition, this demo offers crucial insights into multi-class classification mechanisms and boundary formation. Neural network learners will appreciate the clear demonstration of how decision surfaces partition feature space, which translates directly to understanding hidden layer transformations in neural architectures. The MATLAB-based code employs efficient matrix operations for distance calculations and classification logic, with visualization functions like contour plotting for boundary display. Despite its concise implementation, the program demonstrates robust practical value through its modular structure - easily modifiable for different classifier algorithms or additional feature dimensions. The code structure allows beginners to understand core classification concepts while providing a foundation for more complex machine learning implementations.