This thesis combines an epistemological concern about induction with a computational exploration of inductive mechanisms. It aims to investigate how inductive performance could be improved by using induction to select appropriate generalisation procedures. The thesis revolves around a meta-learning system, called extscThe Entrencher, designed to investigate how inductive performances could be improved by using induction to select appropriate generalisation procedures. The performance of extscThe Entrencher is discussed against the background of epistemological issues concerning induction, such as the role of theoretical vocabularies and the value of simplicity. After an introduction about machine learning and epistemological concerns with induction, Part I looks at learning mechanisms. It reviews some concepts and issues in machine learning and presents extscThe Entrencher. The system is the first attempt to develop a learning system that induces over learning mechanisms through algorithmic representations of tasks. Part II deals with the need for theoretical terms in induction. Experiments where extscThe Entrencher selects between different strategies for representation change are reported. The system is compared to other methods and some conclusions are drawn concerning how best to use the system. Part III considers the connection between simplicity and inductive success. Arguments for Occam's razor are considered and experiments are reported where extscThe Entrencher is used to select when, how and how much a decision tree needs to be pruned. Part IV looks at some philosophical consequences of the picture of induction that emerges from the experiments with extscThe Entrencher and goes over the motivations for meta-learning. Based on the picture of induction that emerges in the thesis, a new position in the scientific realism debate, transcendental surrealism, is proposed and defended. The thesis closes with some considerations concerning induction, justification and epistemological naturalism.