Explore MATLAB source code curated for "分类算法" with clean implementations, documentation, and examples.
Implementation of CSP feature extraction and classification algorithm for motor imagery EEG signals on MATLAB platform, with extendable voting mechanism for multi-class classification
This approach for shape recovery from texture and texture applications utilizes a classification algorithm based on discrete wavelet frame modulus maxima co-occurrence matrices, effectively restoring images with large-area缺损 through sophisticated feature analysis and pattern reconstruction techniques.
Detailed examination of the MVU algorithm, a standard classification approach with discussion on efficient implementation using key functions and computational strategies.
[MATLAB] Implementation of Support Vector Machine (SVM) Algorithm for Classification with practical coding examples and parameter optimization techniques
The main.m function serves as the primary entry point, while HK.m contains the core classification algorithm. Training patterns (w1, w3) and unclassified patterns (w2, w4) are loaded from the Patterns.mat file, which contains four distinct samples. The algorithm processes these patterns through feature extraction and decision boundaries to classify unknown samples.
Early detection of arrhythmia is critical for cardiac patients through electrocardiogram (ECG) signal analysis and feature extraction. This study implements three distinct feature extraction algorithms—Fast Fourier Transform (FFT), Autoregressive (AR) Modeling, and Principal Component Analysis (PCA)—combined with an Artificial Neural Network (ANN) classifier. The PCA-based system achieved superior accuracy of 92.7083% using 3-second ECG intervals, outperforming reference methods (84.4%). The approach demonstrates scalable applicability for arrhythmia classification and potential cardiac disease prediction.
Boosting Classification Algorithm Program - Implementation and Technical Overview
MATLAB source code for implementing Support Vector Machine (SVM) classification algorithm with detailed implementation insights
Image segmentation represents a critical research domain in image processing, serving as the foundational step for image analysis. Its primary objective is to extract target objects of interest from images. While numerous scholars worldwide have proposed various segmentation methodologies, these approaches generally lack universality across diverse image types, typically demonstrating effectiveness only for specific image categories. Support Vector Machine (SVM), a classification technique rooted in statistical learning theory, has gained extensive application across multiple domains including pattern recognition, data classification, and image segmentation. Renowned for its strong generalization capabilities, SVM has emerged as a prevalent trend in image segmentation implementations, consistently delivering superior segmentation outcomes. This article explores SVM-based image segmentation methodologies with particular focus on implementation approaches and algorithmic enhancements.
Integrated UCI Dataset for AI Classification Algorithm Simulation and Evaluation