Cross validation in matlab

What are the steps to performing cross validation on labels of data to get the accuracy of the results?

Answer 1

[Greg Heath]

Repeat until the parameter estimates converges

1.Randomly divide the data into 10 subsets

2.For each subset

a. Use the remaining 9 subsets to design a model
b. Test the model with the holdout subset
c. Update the average and standard deviation of 
 the holdout test set error.
d. If std < thresh1 or std < thresh2*avg, stop.

Hope this helps.

Thank you for formally accepting my answer.

Greg

Answer 2

[Ilya]

The Statistics Toolbox provides utilities for cross-validation. If you are using R2011a or later, take a look at ClassificationTree.fit, ClassificationDiscriminant.fit, ClassificationKNN.fit and fitensemble. Notice the 'crossval' parameter and other related parameters. If you are working in an older release or not using any of these classifiers, the crossval function is a generic utility for that purpose.

[Lester Lim]

Is there any other way to do it without stats toolbox?