Abstract
A key step of supervised learning is testing whether a can- didate hypothesis covers a given example. When learning in first order logic languages, the covering test is equivalent to a Constraint Satisfaction Problem (CSP). For critical values of some order parameters, CSPs present a phase pransition, that is, the probability of finding a solution abruptly drops from almost 1 to almost 0, and the complexity drama- tically increases. This paper analyzes the complexity and feasibility of learning in first order logic languages with respect to the phase transition of the covering test.
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Giordana, A., Saitta, L., Sebag, M., Botta, M. (2000). Can Relational Learning Scale Up?. In: Raś, Z.W., Ohsuga, S. (eds) Foundations of Intelligent Systems. ISMIS 2000. Lecture Notes in Computer Science(), vol 1932. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-39963-1_4
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DOI: https://doi.org/10.1007/3-540-39963-1_4
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