Rule Extraction for Transfer Learning
نویسندگان
چکیده
Typically rule extraction is done for the purposes of human interpretation. However, there are other possible applications of rule extraction. One practical application is transfer learning, in which knowledge learned in one task is used to aid in learning a related task. The extracted rules, which explain the learned solution to the first task, can be considered advice on how to approach the second task. Transfer learning, besides being desirable in its own right, could be viewed as another way to evaluate extracted rules. That is, how well extracted knowledge transfers to a related task is a potential way of judging the value of the rule extraction algorithm. Thus transfer can be used as an alternative to traditional measures such as complexity and faithfulness to the original model. While this method is more objective and more computational than some of the traditional measures, it requires a trusted algorithm for making use of extracted knowledge. This chapter discusses transfer learning via advice taking, in particular for reinforcement learning (RL) tasks that use support vector machines (SVMs) as function approximators. After some background information on transfer, advice, and RL with SVMs, it describes two methods for rule extraction in this context and presents a case study from our recent research.
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