TABLE OF CONTENTS Ricardo Vilalta
University of Houston
Christophe Giraud-Carrier
Brigham Young University
Pavel Brazdil
University of Porto
| Abstract | The field of meta-learning has as one of its primary goals the understanding of the interaction between the mechanism of learning and the concrete contexts in which that mechanism is applicable. The field has seen a continuous growth in the past years with interesting new developments in the construction of practical model-selection assistants, task-adaptive learners, and a solid conceptual framework. In this chapter we give an overview of different techniques necessary to build meta-learning systems. We begin by describing an idealized meta-learning architecture comprising a variety of relevant component techniques. We then look at how each technique has been studied and implemented by previous research. In addition we show how meta-learning has already been identified as an important component in real-world applications. |
| Keywords: | Meta-learning |
We are used to thinking of a learning system as a rational agent capable of adapting to a specific environment by exploiting knowledge gained through experience; encountering multiple and diverse scenarios sharpens the ability of the learning system to predict the effect produced from selecting a particular course of action. In this case, learning is made manifest because the quality of the predictions normally improves with an increasing number of scenarios or examples. Nevertheless, if the predictive mechanism were to start afresh on different tasks, the learning system would find itself at a considerable disadvantage; learning systems capable of modifying their own predictive mechanism would soon outperform...
