Knowledge Reuse in Multiple Classi erSystems ?
نویسنده
چکیده
We introduce a framework for the reuse of knowledge from previously trained clas-siiers to improve performance in a current and possibly related classiication task. The approach used is exible in the type and relevance of reused classiiers and is also scalable. Experiments on public domain data sets demonstrate the usefulness of this approach when one is faced with very few training examples or very noisy training data.
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