Towards learning with objects in a hierarchical representation
- In most supervised learning tasks, objects are perceived as a collection of fixed attribute values. In this work, we try to extend this notion to a hierarchy of attribute sets with different levels of quality. When we are given the objects in this representation, we might consider to learn from most examples at the lowest quality level and only to enhance a few examples for the classification algorithm. We propose an approach for selecting those interesting objects and demonstrate its superior performance to random selection.
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