Genetic Algorithm for Feature Selection in Pattern Classification
Genetic Algorithm for feature selection in pattern classification, using the MNIST dataset as a practical example with detailed implementation routines and code explanations.
Pattern Classification Application Using BP Learning Algorithm
Pattern Classification Using Momentum BP Learning Algorithm - This implementation applies the momentum backpropagation algorithm for classifying the classic UCI Iris dataset, achieving fast processing speed and high accuracy. The iris.arff file is the original dataset file that can be opened using Weka data mining software. Iris.csv is the data file converted through Weka software for source code reading. To execute the algorithm, simply place the source file Iris_classify.m and Iris.csv in MATLAB's work directory and run directly.
BP Neural Network Implementation in MATLAB
MATLAB examples for BP Neural Networks (primarily used for function approximation and pattern classification) with detailed code implementation
RBF Neural Network Simulation (Primarily for Function Fitting and Pattern Classification)
MATLAB Example Implementation of RBF Neural Network for Function Approximation and Pattern Classification
Support Vector Machine Toolbox (MATLAB)
This MATLAB toolbox provides comprehensive support vector machine (SVM) functionality for pattern classification, pattern recognition, and machine learning applications, featuring customizable kernel functions and optimization algorithms for robust model training.
EM (Expectation Maximization) Algorithm Implementation in MATLAB
MATLAB implementation of the EM (Expectation Maximization) algorithm designed for pattern classification, featuring probabilistic modeling and iterative optimization
Feature Selection in Pattern Classification
Feature selection in pattern classification, with practical implementation guidance and algorithm insights for reference.
Support Vector Machine Toolbox for MATLAB
Support Vector Machine Toolbox for MATLAB