用R对数据集germandredit分别使用感知机法，K近邻法、朴素贝叶斯法、决策树法、线性支持向量机法构建分类器

首先，需要先加载所需的库和数据集：

library(e1071) # 支持向量机
library(class) # K近邻
library(naivebayes) # 朴素贝叶斯
library(rpart) # 决策树
library(perceptron) # 感知机

data(germancredit)

然后，需要将数据集分成训练集和测试集：

set.seed(123)
trainIndex <- sample(1:nrow(germancredit), 0.7*nrow(germancredit))
train <- germancredit[trainIndex, ]
test <- germancredit[-trainIndex, ]

接下来，分别使用感知机法、K近邻法、朴素贝叶斯法、决策树法和线性支持向量机法构建分类器：

# 感知机法
model_perceptron <- perceptron(formula = Creditability ~ ., data = train)
pred_perceptron <- predict(model_perceptron, newdata = test)

# K近邻法
model_knn <- knn(train = train[, -1], test = test[, -1], cl = train$Creditability, k = 5)
pred_knn <- as.numeric(model_knn)

# 朴素贝叶斯法
model_nb <- naiveBayes(Creditability ~ ., data = train)
pred_nb <- predict(model_nb, newdata = test)

# 决策树法
model_tree <- rpart(Creditability ~ ., data = train, method = "class")
pred_tree <- predict(model_tree, newdata = test, type = "class")

# 线性支持向量机法
model_svm <- svm(Creditability ~ ., data = train, kernel = "linear")
pred_svm <- predict(model_svm, newdata = test)

最后，可以使用混淆矩阵和准确率等指标来评估分类器的性能：

# 感知机法
table(pred_perceptron, test$Creditability)
# 准确率
mean(pred_perceptron == test$Creditability)

# K近邻法
table(pred_knn, test$Creditability)
# 准确率
mean(pred_knn == test$Creditability)

# 朴素贝叶斯法
table(pred_nb, test$Creditability)
# 准确率
mean(pred_nb == test$Creditability)

# 决策树法
table(pred_tree, test$Creditability)
# 准确率
mean(pred_tree == test$Creditability)

# 线性支持向量机法
table(pred_svm, test$Creditability)
# 准确率
mean(pred_svm == test$Creditability)

其中，混淆矩阵可以用来展示分类器在不同类别上的表现，准确率则是分类器的整体性能指标。