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 | 1 | initial version |
imho, the problem with your current approach is, that SVM1 does not know person 3 as a negative, though it should.
try to train SVM1 with features from class1 as positives, and features from all other classes as negatives. (all labeled -1 , irrespective of their real id, here they are just 'the negatives')
| | 2 | No.2 Revision |
imho, the problem with your current approach is, that SVM1 does not know person 3 as a negative, though it should.
try to train SVM1 with features from class1 as positives, and features from all other classes as negatives. (all labeled -1 , irrespective of their real id, here they are just 'the negatives')
last but not least, if your features are long enough, consider using one multi-class svm, too.
| | 3 | No.3 Revision |
imho, the problem with your current approach is, that SVM1 does not know person 3 as a negative, though it should.
try to train SVM1 with features from class1 as positives, and features from all other classes as negatives. (all labeled -1 , irrespective of their real id, here they are just 'the negatives')
last but not least, if your features are long enough, consider using one multi-class svm, too.too (instead of your current multi-svm-on-on-class-versus-all).
| | 4 | No.4 Revision |
imho, the problem with your current approach is, that SVM1 does not know person 3 as a negative, though it should.
try to train SVM1 with features from class1 as positives, and features from all other classes as negatives. (all labeled -1 , irrespective of their real id, here they are just 'the negatives')
last but not least, if your features are long enough, consider using one multi-class svm, too (instead of your current multi-svm-on-on-class-versus-all).multi-svm-one-class-versus-all approach).
