Access the full text.
Sign up today, get DeepDyve free for 14 days.
Loading next page...
/lp/wiley/artificial-neural-networks-as-a-tool-for-archaeological-data-analysis-eRYeU9kGn2
References (11)
-
L. Kryger (1981)
Interpretation of analytical chemical information by pattern recognition methods-a survey.Talanta, 28 12
-
M. Baxter (1989)
Multivariate-Analysis of Data on Glass Compositions - A Methodological NoteArchaeometry, 31
-
T. Beier, H. Mommsen (1994)
MODIFIED MAHALANOBIS FILTERS FOR GROUPING POTTERY BY CHEMICAL COMPOSITIONArchaeometry, 36
-
J. Linderholm, E. Lundberg (1994)
Chemical Characterization of Various Archaeological Soil Samples using Main and Trace Elements determined by Inductively Coupled Plasma Atomic Emission SpectrometryJournal of Archaeological Science, 21
-
M. Baxter (1992)
STATISTICAL ANALYSIS OF CHEMICAL COMPOSITIONAL DATA AND THE COMPARISON OF ANALYSESArchaeometry, 34
-
J. Zupan, J. Gasteiger (1991)
Neural networks: A new method for solving chemical problems or just a passing phase?Analytica Chimica Acta, 248
-
J. Leonard, M. Kramer (1991)
Radial basis function networks for classifying process faultsIEEE Control Systems, 11
-
M. Baxter (1991)
PRINCIPAL COMPONENT AND CORRESPONDENCE ANALYSES OF GLASS COMPOSITIONS: AN EMPIRICAL STUDYArchaeometry, 33
-
S. Bell, W. Mead, R. Jones, G. Eiceman, R. Ewing (1993)
Connectionist hyperprism neural network for the analysis of ion mobility spectra: An empirical evaluationJ. Chem. Inf. Comput. Sci., 33
-
Leonard Leonard, Kramer Kramer (1991)
Radial basis functions for classifying process faultsIEEE Control Systemx., 11
-
R. Hedges, C. Salter (1979)
SOURCE DETERMINATION OF IRON CURRENCY BARS THROUGH ANALYSIS OF THE SLAG INCLUSIONSArchaeometry, 21
- Publisher
- Wiley
- Copyright
- Copyright © 1998 Wiley Subscription Services, Inc., A Wiley Company
- ISSN
- 0003-813X
- eISSN
- 1475-4754
- DOI
- 10.1111/j.1475-4754.1998.tb00829.x
- Publisher site
- See Article on Publisher Site
Abstract
Artificial neural networks (ANN) offer a supplement to traditional classification algorithms applied to archaeological data. Because of their flexible architecture, non‐linearity and independence from the distribution of the underlying data, neural networks have unique advantages for such applications. In particular, ANN models are well suited for use with the sparse data sets common in archaeological work. Combining multivariate techniques with networks for data validation, pre‐processing and classification exploits the merits of both and provides a comprehensive approach to the analysis and classification of archaeological data.
Journal
Archaeometry – Wiley
Published: Feb 1, 1998