分别对朴素贝叶斯分类器、SVM分类器、最小近邻、logistics回归对不同字体数字手写体识别的数对手写体进行识别的Matlabd的代码实现，并计算出准确率

好的，以下是使用不同分类器对手写数字进行识别的Matlab代码实现，包括朴素贝叶斯分类器、SVM分类器、最小近邻算法和logistics回归，并计算其准确率。

1. 朴素贝叶斯分类器：

% Load MNIST dataset
load('mnist.mat');

% Split dataset into training and testing sets
train_idx = randperm(size(X_train, 1), 5000);
test_idx = randperm(size(X_test, 1), 1000);

X_train = X_train(train_idx,:);
y_train = y_train(train_idx);

X_test = X_test(test_idx,:);
y_test = y_test(test_idx);

% Train Naive Bayes classifier
nb = fitcnb(X_train, y_train);

% Test classifier and compute accuracy
pred = predict(nb, X_test);
acc = sum(pred == y_test) / numel(y_test);

fprintf('Naive Bayes Accuracy: %.2f%%\n', acc*100);

2. SVM分类器：

% Load MNIST dataset
load('mnist.mat');

% Split dataset into training and testing sets
train_idx = randperm(size(X_train, 1), 5000);
test_idx = randperm(size(X_test, 1), 1000);

X_train = X_train(train_idx,:);
y_train = y_train(train_idx);

X_test = X_test(test_idx,:);
y_test = y_test(test_idx);

% Train SVM classifier
svm = fitcecoc(X_train, y_train);

% Test classifier and compute accuracy
pred = predict(svm, X_test);
acc = sum(pred == y_test) / numel(y_test);

fprintf('SVM Accuracy: %.2f%%\n', acc*100);

3. 最小近邻算法：

% Load MNIST dataset
load('mnist.mat');

% Split dataset into training and testing sets
train_idx = randperm(size(X_train, 1), 5000);
test_idx = randperm(size(X_test, 1), 1000);

X_train = X_train(train_idx,:);
y_train = y_train(train_idx);

X_test = X_test(test_idx,:);
y_test = y_test(test_idx);

% Train k-NN classifier
knn = fitcknn(X_train, y_train);

% Test classifier and compute accuracy
pred = predict(knn, X_test);
acc = sum(pred == y_test) / numel(y_test);

fprintf('k-NN Accuracy: %.2f%%\n', acc*100);

4. logistics回归：

% Load MNIST dataset
load('mnist.mat');

% Split dataset into training and testing sets
train_idx = randperm(size(X_train, 1), 5000);
test_idx = randperm(size(X_test, 1), 1000);

X_train = X_train(train_idx,:);
y_train = y_train(train_idx);

X_test = X_test(test_idx,:);
y_test = y_test(test_idx);

% Train logistics regression classifier
lr = fitglm(X_train, y_train, 'Distribution', 'binomial');

% Test classifier and compute accuracy
pred = predict(lr, X_test);
acc = sum(pred == y_test) / numel(y_test);

fprintf('Logistics Regression Accuracy: %.2f%%\n', acc*100);

请注意，这些代码示例仅仅是演示如何使用不同的分类器进行手写数字识别。您可以根据需要更改参数和特征提取器来提高准确性。