Learning from Queries and Examples with Tree-structured Bias
نویسنده
چکیده
Incorporating declarative bias or prior knowledge into learning is an active research topic in machine learning. Tree-structured bias speciies the prior knowledge as a tree of \relevance" relationships between attributes. This paper presents a learning algorithm that implements tree-structured bias, i.e., learns any target function probably approximately correctly from random examples and membership queries if it obeys a given tree-structured bias. The theoretical predictions of the paper are empirically validated.
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