MATLAB Implementation for Face Recognition
Face Recognition Training Sample Based on MATLAB with Algorithm Implementation Details
Extraction of Eigenvalues and Eigenvectors, Training Samples, and Final Recognition
This implementation features eigenvalue/eigenvector extraction, training sample processing, and final recognition stages. The program achieves high-performance levels capable of handling classification and regression tasks in pattern recognition domains.
Sparse Representation-Based Face Recognition
Sparse representation-based face recognition using the ORL database, containing 40 subjects with 10 images each. The implementation randomly selects training samples while using the remaining images as test samples. The final recognition accuracy is calculated as the average of 20 independent trials to ensure statistical reliability.
Decision Boundary Identification Using Minimum Distance Classification
Implementation of minimum distance classification in MATLAB to identify decision boundaries for discriminating between two known classes of training samples, with analysis of classification error rates and algorithmic performance metrics.
MATLAB Source Code Implementation for Training Samples in Support Vector Machines
MATLAB source code implementation for training sample processing in Support Vector Machine (SVM) classification algorithm
Neural Network Classification in MATLAB
Implementing neural network classification functionality in MATLAB by inputting training and test samples for model training and subsequent classification. This simple neural network algorithm is ideal for beginners, featuring clear implementation steps and basic pattern recognition capabilities.
Parzen Window Classification in Pattern Recognition with Simulation Example
A simple simulation of Parzen window classification in pattern recognition, where female.txt and male.txt serve as training samples and test.txt contains test samples. This implementation demonstrates excellent classification performance and provides valuable insights for beginners learning pattern recognition, including practical code structure and algorithm implementation details.
Wavelet Neural Network Implementation
A comprehensive MATLAB implementation of wavelet neural networks featuring both single-dimensional and multi-dimensional network models. The code includes an enhanced initialization algorithm and requires only training data replacement for different applications. This implementation provides valuable support for academic paper writing and research projects.
Source Code Implementation of 0-9 Digit Recognition System Using Backpropagation Neural Network
MATLAB source code implementing a digit recognition system for numbers 0-9 using backpropagation neural networks, featuring a user-friendly interface, training samples, and noisy digit image processing capabilities.
Reading Images from the Standard Handwritten Digit Database
Loading images from the standard handwritten digit database containing 60,000 training samples and 10,000 testing samples for machine learning implementation