Abstract
In the present paper a data-driven approach to learning is described. The approach is discussed in the framework of the Net-Clause Language (NCL), which is also outlined. NCL is aimed at building network models and describes distributed computational schemes. It also exhibits sound semantics as a data-driven deductive system. The proposed learning scheme falls in the class of methods for learning from examples and the learning strategy used is instance-to-class generalization. Two basic examples are discussed giving the underlying ideas of using NCL for inductive concept learning and learning semantic networks.
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© 1991 Springer-Verlag Berlin Heidelberg
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Markov, Z. (1991). An approach to data-driven learning. In: Jorrand, P., Kelemen, J. (eds) Fundamentals of Artificial Intelligence Research. FAIR 1991. Lecture Notes in Computer Science, vol 535. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-54507-7_11
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DOI: https://doi.org/10.1007/3-540-54507-7_11
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