I am trying to build a knn clasiffier with cross validation in Matlab. Because of my MATLAB version I have used knnclassify() in order to build the classifier (classKNN = knnclassify (sample_test, sample_training, training_label)).
I am not capable to use crossval() with that.
There are two ways to perform the K-Nearest Neighbour in Matlab. The first one is by using knnclassify() as you did. However, this function will return the predicted labels and you cannot use crossval() with this. The cross-validation is performed on a model, not on its results. In Matlab, the model is described by an object.
crossval() only works with objects (classifier objects, be it K-NN, SVM and so on...). In order to create the so-called nearest-neighbor classification object you need to use the fitcknn() function. Given the Training Set and the Training Labels as input (in this order), such function will return your object, which you can give as input in crossval().
There's only one thing left though: how do I predict the labels for my validation set? In order to do this, you need to use the predict() function. Given the model (kNN object) and the Validation Set as input (again, in this order), such function will return (as in knnclassify()) the predicted labels vector.