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In many machine learning tasks, unlabled data abounds, but expert-generated labels are scarce. Consider the process of learning to build a classier for the Sloan Digital Sky Survey (http://www.sdss.org/) so that each astronomical observation may be assigned its class (e.g. “pinwheel galaxy”, “globular galaxy”, “quasar”, “colliding galaxies”, “nebula”, etc.). The SDSS contains 230 million astronomical...
Features are the main rule building blocks for rule learning algorithms. They can be simple tests for attribute values or complex logical terms representing available domain knowledge. In contrast to common practice in classification rule learning, we argue that the separation of feature construction and rule construction processes has a theoretical and practical justification. Explicit usage of features...
The goal of transfer learning is to speed up learning in a new task by transferring knowledge from one or more related source tasks. We describe a transfer method in which a reinforcement learner analyzes its experience in the source task and learns rules to use as advice in the target task. The rules, which are learned via inductive logic programming, describe the conditions under which an action...
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