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Using background knowledge to build multistrategy learners
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DOI：10.1023/A:1007313824964.png)
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摘要
En 中文
This paper discusses the role that background knowledge can play in building flexible multistrategy learning systems. We contend that a variety of learning strategies can be embodied in the background knowledge provided to a general purpose learning algorithm. To be effective, the general purpose algorithm must have a mechanism for learning new concept descriptions that can refer to knowledge provided by the user or learned during some other task. The method of knowledge representation is a central problem in designing such a system since it should be possible to specify background knowledge in such a way that the learner can apply its knowledge to new information.
Keyword:
multistrategy learning
inductive logic programming
background knowledge
knowledge representation
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