Abstract
Common inductive learning strategies offer the tools for knowledge acquisition, but possess some inherent limitations due to the use of fixed bias during the learning process. To overcome limitations of such base-learning approaches, a novel research trend is oriented to explore the potentialities of meta-learning, which is oriented to the development of mechanisms based on a dynamical search of bias. This could lead to an improvement of the base-learner performance on specific learning tasks, by profiting of the accumulated past experience. As a significant set of I/O data is needed for efficient base-learning, appropriate meta-data characterization is of crucial importance for useful meta-learning. In order to characterize meta-data, firstly a collection of meta-features discriminating among different base-level tasks should be identified. This paper focuses on the characterization of meta-data, through an analysis of meta-features that can capture the properties of specific tasks to be solved at base level. This kind of approach represents a first step toward the development of a meta-learning system, capable of suggesting the proper bias for base-learning different specific task domains.
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Castiello, C., Castellano, G., Fanelli, A.M. (2005). Meta-data: Characterization of Input Features for Meta-learning. In: Torra, V., Narukawa, Y., Miyamoto, S. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2005. Lecture Notes in Computer Science(), vol 3558. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11526018_45
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DOI: https://doi.org/10.1007/11526018_45
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