Sciweavers

ANNPR
2008
Springer

Partial Discriminative Training of Neural Networks for Classification of Overlapping Classes

14 years 25 days ago
Partial Discriminative Training of Neural Networks for Classification of Overlapping Classes
In applications such as character recognition, some classes are heavily overlapped but are not necessarily to be separated. For classification of such overlapping classes, either discriminating between them or merging them into a metaclass does not satisfy. Merging the overlapping classes into a metaclass implies that within-metaclass substitution is considered as correct classification. For such classification problems, I propose a partial discriminative training (PDT) scheme for neural networks, in which, a training pattern of an overlapping class is used as a positive sample of its labeled class, and neither positive nor negative sample for its allied classes (classes overlapping with the labeled class). In experiments of handwritten letter recognition using neural networks and support vector machines, the PDT scheme mostly outperforms crosstraining (a scheme for multi-labeled classification), ordinary discriminative training and metaclass classification.
Cheng-Lin Liu
Added 12 Oct 2010
Updated 12 Oct 2010
Type Conference
Year 2008
Where ANNPR
Authors Cheng-Lin Liu
Comments (0)