文件名称:KNN
介绍说明－－下载内容均来自于网络，请自行研究使用
Implement the K nearest neighbor algorithm by your own instead of using available software.
2. Use K-fold cross validation to generate training and testing datasets. You should try different K values (3~8) to see how they affect your result.
3. Train the classifier using your training dataset, and test the classifier using your testing dataset.
4. Repeat the experiment (Step 2 and Step 3) 30 times. For each time, you need to record the training data accuracy and testing data accuracy. Finally, you can obtain the average training data accuracy and average testing data accuracy.
2. Use K-fold cross validation to generate training and testing datasets. You should try different K values (3~8) to see how they affect your result.
3. Train the classifier using your training dataset, and test the classifier using your testing dataset.
4. Repeat the experiment (Step 2 and Step 3) 30 times. For each time, you need to record the training data accuracy and testing data accuracy. Finally, you can obtain the average training data accuracy and average testing data accuracy.
(系统自动生成,下载前可以参看下载内容)
下载文件列表
KNN\Assignment 2 Classification.doc
...\KNN_1.m
...\KNN_2.m
...\Wine.csv
KNN