Algorithm quasi-optimal (AQ) learning

Guido Cervone, Pasquale Franzese, Allen P K Keesee

Research output: Contribution to journalReview article

21 Citations (Scopus)

Abstract

The algorithm quasi-optimal (AQ) is a powerful machine learning methodology aimed at learning symbolic decision rules from a set of examples and counterexamples. It was first proposed in the late 1960s to solve the Boolean function satisfiability problem and further refined over the following decade to solve the general covering problem. In its newest implementations, it is a powerful but yet little explored methodology for symbolic machine learning classification. It has been applied to solve several problems from different domains, including the generation of individuals within an evolutionary computation framework. The current article introduces the main concepts of the AQ methodology and describes AQ for source detection(AQ4SD), a tailored implementation of the AQ methodology to solve the problem of finding the sources of atmospheric releases using distributed sensor measurements. The AQ4SD program is tested to find the sources of all the releases of the prairie grass field experiment .

Original languageEnglish (US)
Pages (from-to)218-236
Number of pages19
JournalWiley Interdisciplinary Reviews: Computational Statistics
Volume2
Issue number2
DOIs
StatePublished - Mar 1 2010

Fingerprint

Methodology
Machine Learning
Distributed Sensor
Field Experiment
Covering Problem
Satisfiability Problem
Evolutionary Computation
Decision Rules
Boolean Functions
Counterexample
Learning
Framework
Concepts

All Science Journal Classification (ASJC) codes

  • Statistics and Probability

Cite this

Cervone, Guido ; Franzese, Pasquale ; Keesee, Allen P K. / Algorithm quasi-optimal (AQ) learning. In: Wiley Interdisciplinary Reviews: Computational Statistics. 2010 ; Vol. 2, No. 2. pp. 218-236.
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Algorithm quasi-optimal (AQ) learning. / Cervone, Guido; Franzese, Pasquale; Keesee, Allen P K.

In: Wiley Interdisciplinary Reviews: Computational Statistics, Vol. 2, No. 2, 01.03.2010, p. 218-236.

Research output: Contribution to journalReview article

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