Cooperation of data-driven and model-based induction methods for relational learning (1993).

Content Provider	CiteSeerX
Author	Sommer, Edgar Gmd, Edgar Sommer
Abstract	Inductive learning in relational domains has been shown to be intractable in general. Many approaches to this task have been suggested nevertheless; all in some way restrict the hypothesis space searched. They can be roughly divided into two groups: data-driven, where the restriction is encoded into the algorithm, and model-based, where the restrictions are made more or less explicit with some form of declarative bias. This paper describes Incy, an inductive learner that seeks to combine aspects of both approaches. Incy is initially data-driven, using examples and background knowledge to put forth and specialize hypotheses based on the "connectivity" of the data at hand. It is model-driven in that hypotheses are abstracted into rule models, which are used both for control decisions in the data-driven phase and for model-guided induction. Key Words: Inductive learning in relational domains, cooperation of data-driven and model-guided methods, implicit and declarative bias. 1 Introduc...
File Format	PDF
Publisher Date	1993-01-01
Access Restriction	Open
Subject Keyword	Model-based Induction Method Relational Learning Inductive Learning Relational Domain Declarative Bias Rule Model Hypothesis Space Inductive Learner Key Word Control Decision Model-guided Induction Model-guided Method Data-driven Phase Background Knowledge Many Approach
Content Type	Text
Resource Type	Article