The Use of Artificial Neural Networks in Projectile Point Typology
| Autor: | Brendan S. Nash, Elton R. Prewitt |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Typology
Archeology 060102 archaeology Artificial neural network business.industry Computer science Process (engineering) Mode (statistics) 06 humanities and the arts 02 engineering and technology Artifact (software development) Field (computer science) Anthropology Pattern recognition (psychology) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing 0601 history and archaeology Artificial intelligence Architecture business |
| Zdroj: | Lithic Technology. 41:194-211 |
| ISSN: | 2051-6185 0197-7261 |
| DOI: | 10.1080/01977261.2016.1184876 |
| Popis: | Regional empirical typologies have received criticisms for characterizing artifact types as immutable, mutually exclusive groups, rather than analytical groups developed for an analytical purposes. The research presented here uses an empirical regional typology to supply data for the creation of analytical units. Developed in the field of computer science as a means to discover complex patterns in data sets, archaeologists can use artificial neural networks to identify analytical patterns in typological data. In this paper we explain what neural networks are and how they work. We show how a specific architecture and training process can be applied to artificial neural networks to create analytical units from empirical units. Finally, we provide four examples to show that the networks will not find a pattern where none exists, and to demonstrate how artificial neural networks can be applied to solve more complex problems. |
| Databáze: | OpenAIRE |
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