Defining Agricultural Regions in Russia: Fuzziness in Multivariate Classification of Historical Data

Borodkin, Leonid

doi:10.3366/hac.1999.11.1-2.31

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“…An alternative to classifications based on Boolean sets (Staničič, Kvamme, 1999) are classifications using fuzzy logic systems (Zadeh, 1965), which are commonly applied to classifications in different areas (Maschner and Stein, 1995;Borodkin, 1999;MacMillan et al, 2000;Barceló , et. al., 2002;Ergin et al, 2003;Barceló , 2005).…”

Section: Introductionmentioning

confidence: 99%

Using multivariate statistics and fuzzy logic system to analyse settlement preferences in lowland areas of the temperate zone: an example from the Polish Lowlands

Jasiewicz

Hildebrandt‐Radke

2009

Journal of Archaeological Science

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Section: Introductionmentioning

confidence: 99%

Using multivariate statistics and fuzzy logic system to analyse settlement preferences in lowland areas of the temperate zone: an example from the Polish Lowlands

Jasiewicz

Hildebrandt‐Radke

2009

Journal of Archaeological Science

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A new GRASS GIS fuzzy inference system for massive data analysis

Jasiewicz

2011

Computers & Geosciences

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Computer Systems that Learn

Barceló

2009

Computational Intelligence in Archaeology

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Inverse problems are among the most challenging in computational and applied science and have been studied extensively (Bunge, 2006; Hensel, 1991; Kaipio & Somersalo, 2004; Kirsch, 1996; Pizlo, 2001; Sabatier, 2000; Tarantola, 2005; Woodbury, 2002). Although there is no precise definition, the term refers to a wide range of problems that are generally described by saying that their answer is known, but not the question. An obvious example would be “Guessing the intentions of a person from her/his behavior.” In our case: “Guessing a past event from its vestiges.” In archaeology, the main source for inverse problems lies in the fact that archaeologists generally do not know why archaeological observables have the shape, size, texture, composition, and spatiotemporal location they have. Instead, we have sparse and noisy observations or measurements of perceptual properties, and an incomplete knowledge of relational contexts and possible causal processes. From this information, an inverse engineering approach should be used to interpret adequately archaeological observables as the material consequence of some social actions.

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Defining Agricultural Regions in Russia: Fuzziness in Multivariate Classification of Historical Data

Cited by 3 publications

References 0 publications

Using multivariate statistics and fuzzy logic system to analyse settlement preferences in lowland areas of the temperate zone: an example from the Polish Lowlands

Using multivariate statistics and fuzzy logic system to analyse settlement preferences in lowland areas of the temperate zone: an example from the Polish Lowlands

A new GRASS GIS fuzzy inference system for massive data analysis

Computer Systems that Learn

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