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Data Driven Knowledge Extraction of Materials Properties

Record type: Conference or Workshop Item (Other)

In this paper the problem of modelling a large commercial materials dataset using advanced adaptive numeric methods is described. The various approaches are briefly outlined, with an emphasis on their characteristics with respect to generalisation, performance and transparency. A highly novel Support Vector Machine (SVM) approach incorporating a high degree of transparency via a full ANalysis Of VAriance (ANOVA) expansion is also used. Using the example of predicting 0.2% proof stress from a set of materials features, we show how the different modelling techniques compare when benchmarked against independent test data.

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Citation

Kandola, J.S., Gunn, S.R., Sinclair, I. and Reed, P.A.S. (1999) Data Driven Knowledge Extraction of Materials Properties At Intelligent Processing and Manufacturing of Materials. , pp. 361-366.

More information

Published date: July 1999
Additional Information: Accepted for Publication at IPMM 99 CD-ROM. Organisation: IEEE
Venue - Dates: Intelligent Processing and Manufacturing of Materials, 1999-07-01
Organisations: Electronic & Software Systems

Identifiers

Local EPrints ID: 252054
URI: http://eprints.soton.ac.uk/id/eprint/252054
PURE UUID: 90d90a25-d044-4e2c-b731-03da09dc10c7

Catalogue record

Date deposited: 29 Nov 2003
Last modified: 18 Jul 2017 10:07

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Contributors

Author: J.S. Kandola
Author: S.R. Gunn
Author: I. Sinclair
Author: P.A.S. Reed

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