Explore MATLAB source code curated for "训练样本" with clean implementations, documentation, and examples.
A comprehensive MATLAB implementation of PCA-based face recognition, featuring eigenvector extraction, training sample processing, and final recognition validation with robust algorithmic components.
This is an SVM classifier designed for classifying training samples in pedestrian detection applications, featuring direct MATLAB implementation with built-in support for key machine learning functions.
AdaBoost classifier MATLAB source code for training samples and implementing classification with weak learner combination algorithms
MATLAB SVM toolbox implementation with excellent usability. Place data in the MATLAB folder, open MATLAB, and execute the sequence: 1. mex setup 2. y 3. 2 4. y to obtain classification accuracy and test result matrix using training and testing samples. The implementation involves compiling C/C++ extensions through mex configuration for optimized SVM computation.
How the K-means Clustering Algorithm Operates with Implementation Steps Step 1: Determine the optimal value of k (number of clusters) through methods like the Elbow Method or Silhouette Analysis. Step 2: Initialize cluster centroids either randomly or systematically using approaches such as K-means++ for better convergence. The algorithm proceeds by iteratively assigning data points to the nearest centroid and recalculating centroid positions.
MATLAB Code Implementation of Particle Swarm Optimization for Backpropagation Neural Network Training
MATLAB source code implementation for training samples in Support Vector Machines, including data preparation, model configuration, and optimization techniques
Implementation of BP neural network for handwritten digit recognition (0-9 digits) with code-oriented architecture and optimization strategies