nan_train_sc Classification Cluster Analysis

NaN Toolbox >> NaN Toolbox > Classification > nan_xval

nan_xval

is used for crossvalidation

Calling Sequence

[R,CC] = nan_xval(D,classlabel)
.. = nan_xval(D,classlabel,CLASSIFIER)
.. = nan_xval(D,classlabel,CLASSIFIER,type)
.. = nan_xval(D,list(classlabel,W),CLASSIFIER)
.. = nan_xval(D,list(classlabel,W,NG),CLASSIFIER)

Parameters

Input:

D:

data features (one feature per column, one sample per row)

Two different encodings are supported:

'SVM','RBF','PSVM','SVM11','SVM:

LIN4','SVM:LIN0','SVM:LIN1','SVM:LIN2','SVM:LIN3','WINNOW'}

W:

weights for each sample (row) in D.

default:

[] (i.e. all weights are 1)

NG:

used to define the type of cross-valdiation

Leave-One-Out-Method (LOOM):

NG = [1:length(classlabel)]' (default)

Leave-K-Out-Method:

NG = ceil([1:length(classlabel)]'/K)

K-fold XV:

NG = ceil([1:length(classlabel)]'*K/length(classlabel))

TYPE:

defines the type of cross-validation procedure if NG is not specified

OUTPUT:

Examples

load_fisheriris;    //builtin iris dataset
C = species;
K = 5; NG = [1:length(C)]'*K/length(C);
[R,CC] = nan_xval(meas,{C,[],NG},'NBC');

See also

Bibliography

[1] R. Duda, P. Hart, and D. Stork, Pattern Classification, second ed.

John Wiley & Sons, 2001.

[2] A. Schlögl, J. Kronegg, J.E. Huggins, S. G. Mason;

Evaluation criteria in BCI research.

(Eds.) G. Dornhege, J.R. Millan, T. Hinterberger, D.J. McFarland, K.-R.Müller;

Towards Brain-Computer Interfacing, MIT Press, 2007, p.327-342

Authors

nan_train_sc Classification Cluster Analysis