Search - k cross validation

Search - k cross validation - List

[Other] KNN DL : 0: 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.
Date : Size : 158kb User : Chang
[Other] classifier DL : 0: 用matlab实现Part1. 实现一个k近邻分类器，Part 2.实现一个最小二乘分类器，Part 3.实现一个支持向量机分类器，Part 4.在不同数据集上使用交叉验证选择各个算法的参数-Part1. Achieve a k-nearest neighbor classifier, Part 2. Achieve a least-squares classifier, Part 3. Implement a support vector machine classifier, Part 4. Different data sets used in the cross-validation to select individual parameters of the algorithm
Date : Size : 4kb User : 张翰晓
[Other] lvq DL : 0: code for lvq and split the data to be train and test by k-fold cross validation with k=5
Date : Size : 1kb User : nicky
[Other] plscv DL : 0: pls交叉验证，可自行设定K-folder交叉验证(pls cross validation)
Date : Size : 6kb User : djdy