Michael Fink

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We describe a framework for learning an object classifier from a single example, by emphasizing relevant dimensions using available examples of related classes. Learning to accurately classify objects from a single training example is often unfeasible due to overfitting effects. However, if the instance representation provides that the distance between each(More)
In this paper we learn heuristic functions that efficiently find the shortest path between two nodes in a graph. We rely on the fact that often, several elementary admissible heuristics might be provided , either by human designers or from formal domain abstractions. These simple heuris-tics are traditionally composed into a new admissible heuristic by(More)
Current object recognition systems aim at achieving two challenging goals: recognizing numerous object classes and learning new object classes from a small number of examples. This paper provides a benchmark for evaluating progress on these fundamental tasks. Several methods have recently proposed to utilize the commonalities between object classes in order(More)
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