Abstract
One of the key issues in so-called multi-strategy learning systems is the degree of freedom and flexibility with which different learning and inference components can be combined. Most of multi-strategy systems only support fixed, tailored integration of the different modules for a specific domain of problems. We will report here our current research on the Massive Memory Architecture (MMA), an attempt to provide a uniform representation framework for inference and learning components supporting flexible, multiple combination of these components. Rather than a specific combination of learning methods, we are interested in an architecture adaptable to different domains where multiple learning strategies (combinations of learning methods) can be programmed or even learned.
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© 1994 Springer-Verlag Berlin Heidelberg
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Plaza, E., Arcos, J.L. (1994). Flexible integration of multiple learning methods into a problem solving architecture. In: Bergadano, F., De Raedt, L. (eds) Machine Learning: ECML-94. ECML 1994. Lecture Notes in Computer Science, vol 784. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-57868-4_84
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DOI: https://doi.org/10.1007/3-540-57868-4_84
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