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 | 1 | initial version |
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
| | 2 | No.2 Revision |
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
| | 3 | No.3 Revision |
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
| | 4 | No.4 Revision |
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