Particle Swarm Optimization for Tuning Support Vector Machine Model Parameters

This MATLAB code implements a cost-sensitive Support Vector Machine (SVM) model optimized by Particle Swarm Optimization (PSO) algorithm, specifically designed for handling imbalanced datasets through automated parameter tuning.

Particle Swarm Optimization for Support Vector Machine Model Parameter Tuning

This file contains MATLAB code implementing Particle Swarm Optimization (PSO) to optimize parameters for a cost-sensitive Support Vector Machine (SVM) model designed specifically for handling imbalanced datasets. The algorithm optimizes three key parameters: C1, C2, and sigma, using swarm intelligence principles for efficient parameter space exploration.

Algorithm for Estimating AR(P) Model Parameters Using Burg's Method

Implementation of Burg's method for AR(P) model parameter estimation, where ef represents forward prediction error, bf denotes backward prediction error, and mse indicates the mean squared error of prediction. The final program output stores prediction errors for all orders in a lower triangular matrix structure, with efficient implementation using reflection coefficients and Levinson-Durbin recursion.