Book chapter
Feature Term Subsumption Using Constraint Programming with Basic Variable Symmetry
Principles and Practice of Constraint Programming, pp 1004-1012
2012
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.
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Details
- Title
- Feature Term Subsumption Using Constraint Programming with Basic Variable Symmetry
- Creators
- Santiago Ontañón - Drexel UniversityPedro Meseguer - Universitat Autònoma de Barcelona
- Publication Details
- Principles and Practice of Constraint Programming, pp 1004-1012
- Series
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg; Berlin, Heidelberg
- Resource Type
- Book chapter
- Language
- English
- Academic Unit
- Computer Science (Computing)
- Scopus ID
- 2-s2.0-84868266950
- Other Identifier
- 991021869109904721