We present a heuristic method for learning error correcting output codes matrices based on a hierarchical partition of the class space that maximizes a discriminative criterion. To achieve this goal, the optimal codeword separation is sacrificed in favor of a maximum class discrimination in the partitions. The creation of the hierarchical partition set is performed using a binary tree. As a result, a compact matrix with high discrimination power is obtained. Our method is validated using the UCI database and applied to a real problem, the classification of traffic sign images. © 2006 IEEE.
|Journal||IEEE Transactions on Pattern Analysis and Machine Intelligence|
|Publication status||Published - 1 Jun 2006|
- Multiclass classification
- Multiple classifiers
- Visual object recognition