Abstract
Feature Terms are a generalization of first-order terms which have been recently received increased attention for their usefulness in structured machine learning applications. One of the main obstacles for their wide usage is that their basic operation, subsumption, has a very high computational cost. Constraint Programming is a very suitable technique to implement that operation, in some cases providing orders of magnitude speed-ups with respect to the standard subsumption approach. In addition, exploiting a basic variable symmetry –that often appears in Feature Terms databases– causes substantial additional savings. We provide experimental results of the benefits of this approach.
Pedro Meseguer is partially supported by the projects TIN2009-13591-C02-02 and Generalitat de Catalunya 2009-SGR-1434.
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Ontañón, S., Meseguer, P. (2012). Feature Term Subsumption Using Constraint Programming with Basic Variable Symmetry. In: Milano, M. (eds) Principles and Practice of Constraint Programming. CP 2012. Lecture Notes in Computer Science, vol 7514. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33558-7_71
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DOI: https://doi.org/10.1007/978-3-642-33558-7_71
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