Abstract
We propose a theoretical framework for specification and analysis of a class of learning problems that arise in open-ended environ- ments that contain multiple, distributed, dynamic data and knowledge sources. We introduce a family of learning operators for precise specifica- tion of some existing solutions and to facilitate the design and analysis of new algorithms for this class of problems. We state some properties of in- stance and hypothesis representations, and learning operators that make exact learning possible in some settings. We also explore some relation- ships between models of learning using different subsets of the proposed operators under certain assumptions.
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Caragea, D., Silvescu, A., Honavar, V. (2001). Analysis and Synthesis of Agents That Learn from Distributed Dynamic Data Sources. In: Wermter, S., Austin, J., Willshaw, D. (eds) Emergent Neural Computational Architectures Based on Neuroscience. Lecture Notes in Computer Science(), vol 2036. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44597-8_39
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DOI: https://doi.org/10.1007/3-540-44597-8_39
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