Visible models for interactive pattern recognition
Pattern Recognition Letters
Estimation, learning, and adaptation: systems that improve with use
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Pattern classification and clustering: A review of partially supervised learning approaches
Pattern Recognition Letters
Analysis of unsupervised template update in biometric recognition systems
Pattern Recognition Letters
| Hi-index | 754.84 |
The output of a simple statistical categorizer is used to improve recognition performance on a homogeneous data set. An array of initial weights contains a coarse description of the various classes; as the system cycles through a set of characters from the same source (a typewritten or printed page), the weights are modified to correspond more closely with the observed distributions. The true identifies of the characters remain inaccessible throughout the training cycle. This experimental study of the effect of the various parameters in the algorithm is based onsim 30 000characters from fourteen different font styles. A fivefold average decrease over the initial rates is obtained in both errors and rejects.