Explore MATLAB source code curated for "回归拟合" with clean implementations, documentation, and examples.
Support Vector Machine (SVM) is a generalized linear classifier that performs binary classification using supervised learning, with its decision boundary defined by the maximum-margin hyperplane derived from training samples. This implementation applies SVM regression to predict concrete compressive strength, featuring verified functionality and practical code implementation.
Supervised Learning Neural Network Regression for Gasoline Octane Number Prediction Using Near-Infrared Spectroscopy Data. For detailed tutorial explanations and high-resolution materials (due to file size limitations), please contact me at 1066146635@qq.com. This implementation covers neural network architecture design, spectral data preprocessing, and regression model training techniques.
(1) SVM is specifically designed for small sample problems, capable of obtaining optimal solutions with limited data samples; implementation involves kernel function selection and parameter optimization; (2) The SVM algorithm ultimately transforms into a quadratic programming problem that theoretically achieves global optimal solutions, contrasting with traditional neural networks' local optimum issues; the optimization process utilizes Lagrange multipliers and sequential minimal optimization (SMO) methods; (3) SVM's topological structure is determined by support vectors, avoiding the trial-and-error network structure determination required in traditional neural networks; this is implemented through support vector identification algorithms that automatically determine the model complexity.
Comprehensive code implementation with detailed explanations, fully tested and ready for deployment. Includes complete regression pipeline with data preprocessing, model training, and performance evaluation.
Support Vector Machine Regression Fitting - Concrete Compressive Strength Prediction with Implementation Details
This resource covers Extreme Learning Machine applications for regression and classification, featuring practical implementations including gasoline octane prediction using near-infrared spectroscopy and iris species recognition with comprehensive code examples for learning purposes.
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.