最新基于MATLAB 2023a的机器学习、深度学习

**最新基于MATLAB2023a的机器学习与深度学习**

MATLAB 是一种强大的数值计算环境，广泛应用于科学计算、信号处理、图像分析等领域。近年来，MATLAB 也成为机器学习和深度学习的理想平台之一。以下是基于 MATLAB2023a 的一些最新的机器学习与深度学习技术。

**1. 支持向量机 (SVM)**支持向量机是一种常见的监督式学习算法，用于分类问题。MATLAB 提供了 `svmtrain` 和 `svmpredict` 函数来实现 SVM 模型训练和预测。

matlab% 加载数据集load fisheriris;

% 将数据集分割为训练集和测试集idx = randperm(size(X,1));
X_train = X(idx(1:100), :);
y_train = y(idx(1:100));
X_test = X(idx(101:end), :);
y_test = y(idx(101:end));

% 训练 SVM 模型model = svmtrain(y_train, X_train);

% 预测测试集结果[~, ~, ~, ~, pred] = svmpredict(y_test, X_test, model);

%评估模型性能accuracy = sum(pred == y_test) / size(y_test,1);
fprintf('Accuracy: %.2f%%
', accuracy *100);

matlab% 加载数据集load iris.mat;

% 将数据集分割为训练集和测试集idx = randperm(size(X,1));
X_train = X(idx(1:100), :);
y_train = y(idx(1:100));
X_test = X(idx(101:end), :);
y_test = y(idx(101:end));

% 定义神经网络结构net = newff(X_train, y_train, [105], 'tansig', 'purelin');

% 训练神经网络模型net.trainParam.epochs =100;
net.trainParam.goal =0.0001;
net = train(net, X_train, y_train);

% 预测测试集结果y_pred = net(X_test);

%评估模型性能accuracy = sum(y_pred == y_test) / size(y_test,1);
fprintf('Accuracy: %.2f%%
', accuracy *100);

matlab% 加载数据集load cifar10.mat;

% 将数据集分割为训练集和测试集idx = randperm(size(X,1));
X_train = X(idx(1:10000), :);
y_train = y(idx(1:10000));
X_test = X(idx(10001:end), :);
y_test = y(idx(10001:end));

% 定义 CNN 模型结构net = newcnn(X_train, y_train, [3232], 'relu', 'softmax');

% 训练 CNN 模型net.trainParam.epochs =10;
net.trainParam.goal =0.0001;
net = train(net, X_train, y_train);

% 预测测试集结果y_pred = net(X_test);

%评估模型性能accuracy = sum(y_pred == y_test) / size(y_test,1);
fprintf('Accuracy: %.2f%%
', accuracy *100);

matlab% 加载数据集load mnist.mat;

% 将数据集分割为训练集和测试集idx = randperm(size(X,1));
X_train = X(idx(1:10000), :);
y_train = y(idx(1:10000));
X_test = X(idx(10001:end), :);
y_test = y(idx(10001:end));

% 定义 AE 模型结构net = newae(X_train, [784256], 'relu', 'tansig');

% 训练 AE 模型net.trainParam.epochs =10;
net.trainParam.goal =0.0001;
net = train(net, X_train);

% 预测测试集结果y_pred = net(X_test);

%评估模型性能accuracy = sum(y_pred == y_test) / size(y_test,1);
fprintf('Accuracy: %.2f%%
', accuracy *100);