Information-Extreme Method for Classification of Observations with Categorical Attributes

Information-Extreme Method for Classification of Observations with Categorical Attributes

An algorithm is proposed for information-extreme machine learning based on the adaptive coding of multitype primary features used in the recognition and optimization of geometric parameters of partitioning the space of secondary (unified) features into equivalence classes in the iterative approximat...

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Veröffentlicht in:Cybernetics and systems analysis 2016-03, Vol.52 (2), p.224-231
Hauptverfasser: Dovbysh, A. S., Moskalenko, V. V., Rizhova, A. S.
Format: Artikel
Sprache:eng
Schlagworte:
Adaptive algorithms
Algorithms
Analysis
Approximation
Artificial Intelligence
Classification
Control
Control systems
Criteria
Cybernetics
Data mining
Equivalence
Machine learning
Mathematical analysis
Mathematics
Mathematics and Statistics
Methods
Optimization
Pattern recognition
Processor Architectures
Software Engineering/Programming and Operating Systems
Studies
Systems Theory
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Beschreibung
Zusammenfassung:An algorithm is proposed for information-extreme machine learning based on the adaptive coding of multitype primary features used in the recognition and optimization of geometric parameters of partitioning the space of secondary (unified) features into equivalence classes in the iterative approximation of the global maximum of an information criterion to its boundary value.
ISSN:1060-0396
1573-8337
DOI:10.1007/s10559-016-9818-1