Output
Knowledge representation
Abstract
Having examined the input to machine learning, we move on to review the types of output that can be generated. We first discuss decision tables, which are perhaps the most basic form of knowledge representation, before considering linear models such as those produced by linear regression. Next we explain decision trees, the most widely used kind of knowledge representation in classic machine learning, before looking at rule sets, which are a popular alternative. We consider classification rules, association rules, and rules with exceptions. We briefly venture into the realm of inductive logic programming, which allows for more complex rules than the practical learning techniques covered in this ...
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