Allocation of oaks to Kraft classes based on linear and nonlinear kernel discriminant variables
Bogna Zawieja; Katarzyna Kaźmierczak
Biometrical Letters (2016)
- Volume: 53, Issue: 1, page 37-46
- ISSN: 1896-3811
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topBogna Zawieja, and Katarzyna Kaźmierczak. "Allocation of oaks to Kraft classes based on linear and nonlinear kernel discriminant variables." Biometrical Letters 53.1 (2016): 37-46. <http://eudml.org/doc/281148>.
@article{BognaZawieja2016,
abstract = {A method of discriminant variable determination was used to visualize the division of oak trees into Kraft classes. Usual discriminant variables and several types of kernel discriminant variables were studied. For this purpose the traits of oak (Quercus L.) trees, measured on standing trees, were used. These traits included height of tree, breast height diameter and crown projection area. The use of the Gaussian kernel and modified Gaussian kernel enabled the clearest division into Kraft classes. In particular, the latter method proved to be the most effective.},
author = {Bogna Zawieja, Katarzyna Kaźmierczak},
journal = {Biometrical Letters},
keywords = {discriminant variables; kernel; Kraft classes; oak (Quercus L.)},
language = {eng},
number = {1},
pages = {37-46},
title = {Allocation of oaks to Kraft classes based on linear and nonlinear kernel discriminant variables},
url = {http://eudml.org/doc/281148},
volume = {53},
year = {2016},
}
TY - JOUR
AU - Bogna Zawieja
AU - Katarzyna Kaźmierczak
TI - Allocation of oaks to Kraft classes based on linear and nonlinear kernel discriminant variables
JO - Biometrical Letters
PY - 2016
VL - 53
IS - 1
SP - 37
EP - 46
AB - A method of discriminant variable determination was used to visualize the division of oak trees into Kraft classes. Usual discriminant variables and several types of kernel discriminant variables were studied. For this purpose the traits of oak (Quercus L.) trees, measured on standing trees, were used. These traits included height of tree, breast height diameter and crown projection area. The use of the Gaussian kernel and modified Gaussian kernel enabled the clearest division into Kraft classes. In particular, the latter method proved to be the most effective.
LA - eng
KW - discriminant variables; kernel; Kraft classes; oak (Quercus L.)
UR - http://eudml.org/doc/281148
ER -
References
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