# Revision history [back]

whether you train 1 multi(22)class SVM's or 22 one-against-all SVM's, the data is always the same.

what differs is the labels, and possibly the svm-params

you will have to 'flatten' your letter-images (via reshape(1,1)), push_back() that into a big data Mat, like this:

data:                       multiclass_labels:   single_class_lables(B)    single_class_labels(A)
l e t t e r A p i c             0                           -1                             1
l e t t e r B p i c             1                            1                            -1
l e t t e r A p i c             1                           -1                            -1
l e t t e r C p i c             2                           -1                            -1
l e t t e r Y p i c             21                          -1                            -1


in the multiclass case, you do one prediction, and use the return value as label,

in the one-against-all case, you do 22 predictions, and use the one that returns a value > 0

whether you train 1 multi(22)class SVM's SVM or 22 one-against-all SVM's, the data is always the same.

what differs is the labels, and possibly the svm-params

you will have to 'flatten' your letter-images (via reshape(1,1)), push_back() that into a big data Mat, like this:

data:                       multiclass_labels:   single_class_lables(B)    single_class_labels(A)
l e t t e r A p i c             0                           -1                             1
l e t t e r B p i c             1                            1                            -1
l e t t e r A p i c             1                           -1                            -1
l e t t e r C p i c             2                           -1                            -1
l e t t e r Y p i c             21                          -1                            -1


in the multiclass case, you do one prediction, and use the return value as label,

in the one-against-all case, you do 22 predictions, and use the one that returns a value > 0

whether you train 1 multi(22)class SVM or 22 one-against-all SVM's, the data is always the same.

what differs is the labels, and possibly the svm-params

you will have to 'flatten' your letter-images (via reshape(1,1)), push_back() that into a big data Mat, like this:

data:                    multiclass_labels:   single_class_lables(B)    single_class_labels(A)
l e t t e r A p i c             0                           -1                             1
l e t t e r B p i c             1                            1                            -1
l e t t e r A p i c             1                           -1                            -1
l e t t e r C p i c             2                           -1                            -1
l e t t e r Y p i c             21                          -1                            -1


in the multiclass case, you do one prediction, and use the return value as label,

in the one-against-all case, you do 22 predictions, and use the one that returns a value > 0

whether you train 1 multi(22)class SVM or 22 one-against-all SVM's, the data is always the same.

what differs is the labels, and possibly the svm-params

you will have to 'flatten' your letter-images (via reshape(1,1)), push_back() that into a big data Mat, like this:

data:                   multiclass_labels:   single_class_lables(B)    single_class_labels(A)
l e t t e r A p i c             0                           -1                             1
l e t t e r B p i c             1                            1                            -1
l e t t e r A p i c             1 0                           -1                            -1
l e t t e r C p i c             2                           -1                            -1
l e t t e r Y p i c             21                          -1                            -1


in the multiclass case, you do one prediction, and use the return value as label,

in the one-against-all case, you do 22 predictions, and use the one that returns a value > 0