Least Multiplier Support Vector Machine (LS-SVM) prediction based on improved moth to flame algorithm

Least Multiplier Support Vector Machine (LS-SVM) prediction based on improved moth to flame algorithm

Least Squares Support Vector Machine (LS-SVM) is a commonly used machine learning algorithm for classification and regression problems. LS-SVM builds a model by solving an optimization problem that minimizes an objective function. The moth to flame algorithm is an optimization algorithm based on the behavior of moths to flame in nature. It finds the optimal solution by simulating the process of moths searching for light sources. This article will introduce the LS-SVM prediction based on the improved moth to flame algorithm and provide the corresponding MATLAB source code.

First, we need to install MATLAB and prepare the corresponding toolkit. In MATLAB, we can use ezyfittoolkits to implement training and prediction of LS-SVM models.

% Step 1: 准备数据
% 假设我们有一个包含n个样本的训练集X和对应的输出向量Y
% X是一个n×m的矩阵,其中每一行代表一个样本,每一列代表一个特征
% Y是一个n×1的向量,代表每个样本的输出

% Step 2: 训练LS-SVM模型
% 使用ezyfit工具包的ezlssvm函数训练模型
model = ezlssvm(X, Y

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Origin blog.csdn.net/2301_78484069/article/details/132806455