Explore MATLAB source code curated for "分类问题" with clean implementations, documentation, and examples.
A comprehensive demonstration program showcasing Support Vector Machine applications in classification problems, featuring both linear and non-linear implementations with practical code examples.
A comprehensive collection of neural network implementation examples in MATLAB. Includes the following programs: single-layer linear neural network implementation, perceptron neuron for complex input vector classification, perceptron-based neural network for complex classification problems, numerical analysis program with MATLAB-GUI, backpropagation network for function approximation source code, and self-organizing feature map application examples.
This is a highly sophisticated SVM algorithm implementation that includes multiple linear and nonlinear kernel functions, capable of solving diverse classification problems with customizable kernel selection and parameter optimization features.
A script file demonstrating the grid search method for identifying optimal parameters in Support Vector Machine (SVM) classification problems, including implementation steps and code references.
Implementation of Adaptive Multiscale Nonlinearities (AMN) Method for Classification Problems in MATLAB
MATLAB code implementation of SMOTE (Synthetic Minority Over-sampling Technique) algorithm for handling class imbalance in classification problems
Implementing Classification Error Variation using Backpropagation (BP) Algorithm with Code-Related Insights
Implementation of a k-Nearest Neighbors (KNN) function for binary classification tasks
Different Types of Linear and Nonlinear Kernels with Implementation Approaches