SVM KNN Classification Algorithm Implementation in MATLAB
K-Nearest Neighbors Classification Algorithm Based on Support Vector Machine (SVM) Implementation
SVM GUI Enables Intuitive Interface Operations for Support Vector Machine Implementation
The SVM GUI facilitates user-friendly interface operations for Support Vector Machines, offering superior capabilities compared to neural networks including classification, recognition, regression analysis, and anomaly detection with robust kernel function implementations.
SVM Classification Code for Wine Category Testing
Support Vector Machine classification implementation for red wine category prediction with feature analysis
SVM Classification Code Implementation for Wine Type Testing
SVM classification algorithm implementation with wine category prediction testing using machine learning techniques
SVM Fault Classification and Other Applications
Comprehensive program examples suitable for self-learning with practical implementation guidance
SVM Classification Code for Red Wine Type Prediction
SVM Classification Implementation with Red Wine Category Testing - A machine learning approach using Support Vector Machines for wine type classification based on chemical properties
GWO-Optimized SVM/SVR with Algorithm Implementation Insights
Grey Wolf Optimizer (GWO) Enhanced Support Vector Machines and Support Vector Regression - Implementation and Performance Analysis
SVM Implementation Code with Python Example
Complete Python implementation of Support Vector Machine (SVM) algorithm using scikit-learn library, including dataset generation, model training, and prediction demonstration.
SVM-Based Regression for Opening Price Prediction
Model Objective: Develop a regression model using Support Vector Machines (SVM) to predict daily opening prices of the SSE Composite Index through regression fitting. Model Assumption: The daily opening price of the SSE Composite Index is assumed to correlate with the previous day's opening price, highest value, lowest value, closing price, trading volume, and trading amount. These six indicators serve as independent variables, while the current day's opening price functions as the dependent variable. Implementation involves feature engineering to normalize these financial indicators and employing SVM regression algorithms (such as SVR) with parameter optimization for accurate time-series forecasting.
PSO Toolbox Program
PSO Toolbox Program for Machine Learning Parameter Optimization