A Probabilistic Iterative Architecture for Classification

M. B. Clausing; Thomas Sudkamp

doi:10.1109/NAECON.1990.112934

A Probabilistic Iterative Architecture for Classification

M. B. Clausing, Thomas Sudkamp

Wright State University

Research output: Contribution to journal › Article › peer-review

Abstract

A classification architecture that uses probabilistic representation of support and conditionalization and expectation for updating belief is presented. The updating is guided by a utility function that determines the type of information to be acquired. Expected entropy is used as the utility measure. The three major components of a classification system are the representation of the domain information, the evidence, and the support updating paradigm. The representative of domain knowledge and evidence is described. A general overview of the classification architecture is given. The computations and assumptions required in this iterative method are presented. A detailed example illustrating the generation of support based on the acquisition of one item of evidence is given.

Original language	American English
Journal	Proceedings of the IEEE 1990 National Aerospace and Electronics Conference, 1990. NAECON 1990
DOIs	https://doi.org/10.1109/NAECON.1990.112934
State	Published - May 1 1990

Keywords

Application software
Artificial intelligence
Calculus
Character generation
Computer architecture
Computer science
Entropy
Joining processes
Probability, Logic

Disciplines

Computer Sciences
Engineering
Mathematics
Physical Sciences and Mathematics

Access to Document

10.1109/NAECON.1990.112934License: Unspecified

http://ieeexplore.ieee.org/document/112934/

Cite this

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AB - A classification architecture that uses probabilistic representation of support and conditionalization and expectation for updating belief is presented. The updating is guided by a utility function that determines the type of information to be acquired. Expected entropy is used as the utility measure. The three major components of a classification system are the representation of the domain information, the evidence, and the support updating paradigm. The representative of domain knowledge and evidence is described. A general overview of the classification architecture is given. The computations and assumptions required in this iterative method are presented. A detailed example illustrating the generation of support based on the acquisition of one item of evidence is given.

KW - Application software

KW - Artificial intelligence

KW - Calculus

KW - Character generation

KW - Computer architecture

KW - Computer science

KW - Entropy

KW - Joining processes

KW - Probability, Logic

UR - https://corescholar.libraries.wright.edu/cse/463

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M3 - Article

JO - Proceedings of the IEEE 1990 National Aerospace and Electronics Conference, 1990. NAECON 1990

JF - Proceedings of the IEEE 1990 National Aerospace and Electronics Conference, 1990. NAECON 1990

ER -

A Probabilistic Iterative Architecture for Classification

Abstract

Keywords

Disciplines

Access to Document

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Cite this