Term Comparisons in First-Order Similarity Measures

The similarity measures used in first-order IBL so far have been limited to the function-free case. In this paper we show that a lot of predictive power can be gained by allowing lists and other terms in the input representation and designing similarity measures that work directly on these structures. We present an improved similarity measure for the first-order instance based learner Ribl that employs the concept of edit distances to efficiently compute distances between lists and terms, discuss its computational and formal properties, and show that it is empirically superior by a wide margin on a problem from the domain of biochemistry.

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