scholarly journals Tree Distance in Answer Retrieval and Parser Evaluation

2005 ◽  
Keyword(s):  
Tree Distance

scholarly journals A Fréchet tree distance measure to compare phylogeographic spread paths across trees

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Susanne Reimering ◽  
Sebastian Muñoz ◽  
Alice C. McHardy
Keyword(s):  
Distance Measure ◽  
Tree Distance

A VLSI algorithm for calculating the tree to tree distance

1993 ◽  
Vol 8 (1) ◽  
pp. 68-76
Author(s):  
Meirui Xu ◽  
Xiaolin Liu
Keyword(s):  
Tree Distance ◽  
Vlsi Algorithm

scholarly journals Parent tree distance-dependent recruitment limitation of native and exotic invasive seedlings in urban forests

2015 ◽  
Vol 19 (2) ◽  
pp. 969-981 ◽  
Author(s):  
L. B. Martínez-García ◽  
O. Pietrangelo ◽  
P. M. Antunes
Keyword(s):  
Urban Forests ◽  
Recruitment Limitation ◽  
Tree Distance ◽  
Parent Tree ◽  
Exotic Invasive

scholarly journals Unexplained Relationships of Height-Diameter of Three Tree Species in a Tropical Forest.

2013 ◽  
Vol 6 (3) ◽  
pp. 196-204
Keyword(s):  
Tree Species ◽  
Coefficient Of Determination ◽  
Multivariate Models ◽  
Forest Reserve ◽  
Permanent Sample Plots ◽  
Tree Distance ◽  
Diameter At Breast Height ◽  
Breast Height ◽  
Bivariate Models

Associations between bivariate variables relative to the unexplained relationships of height-Dbh (diameter at breast height) models were investigated. Seven permanent sample plots measuring 40m by 250m at Omo Forest Reserve were used to assess the relationships between height and diameter at breast height of three tree species as affected by the variables of neighbouring trees. The result showed differences in the coefficient of determination of the bivariate models and multivariate models. The models arrived at for each of the species and for the bivariate models are: Scottelia coriaceae: Ht = 2.59 + 0.473D+ 0.0012D2 for 5cm ≤ D ≤100cm (R2 = 0.70) Sterculia rhinopetala: Ht = 5.96 + 0.467D+ 0.00296D2 for 5cm ≤ D ≤100cm (R2 = 0.77) Strombosia pustulata: Ht = 2.02 + 0.722D+ 0.00581D2 for 5cm ≤ D ≤ 60cm (R2 = 0.81) Where both Ht and D are height and Diameter at breast height. While on the other hand the multivariate models that considered the effect of neighbouring trees are: Scottelia coriaceae: 2 1 2 3 4 Ht = 3.74 + 0.41x −1.14x + 0.205x +1.278x (R = 0.723) Sterculia rhnopetala: 2 1 2 3 4 Ht = 6.18 + 0.2601x +1.163x + 0.438x − 0.442x (R = 0.608) Strombosia pustalata: 2 1 2 3 4 Ht = 6.84 + 0.399x − 0.318x − 0.138x − 0.838x (R = 0.650) x1 = diameter at breast height, x2 = Mean neighbouring tree distance, x3 = Frequency of the neighbouring tree and x4 = Position of the crown.

scholarly journals A Hierarchical Tree Distance Measure for Classification

2017 ◽  
Author(s):  
Kent Munthe Caspersen ◽  
Martin Bjeldbak Madsen ◽  
Andreas Berre Eriksen ◽  
Bo Thiesson
Keyword(s):  
Distance Measure ◽  
Hierarchical Tree ◽  
Tree Distance

scholarly journals A Vectorial Tree Distance Measure

2021 ◽  
Author(s):  
Avner Priel ◽  
Boaz Tamir
Keyword(s):  
Phylogenetic Trees ◽  
Distance Measure ◽  
Lexicographic Order ◽  
Distance Measures ◽  
Tree Distance ◽  
Hierarchical Trees ◽  
Distance Vector ◽  
The Difference ◽  
Number Of Branches

Abstract A vectorial distance measure for trees is presented. Given two trees, we align the trees from their centers outwards, starting from the root-branches, to make the next level as similar as possible. The algorithm is recursive; condition on the alignment of the root-branches we align the sub-branches, thereafter each alignment is conditioned on the previous one. We define a minimal alignment under a lexicographic order which follows the intuition that the differences between the two trees closer to their cores dominate their differences at a higher level. Given such a minimal alignment, the difference in the number of branches calculated at any level defines the entry of the distance vector at that level. We compare our algorithm to other well-known tree distance measures in the task of clustering sets of phylogenetic trees. We use the TreeSimGM simulator for generating stochastic phylogenetic trees. The vectorial tree distance can successfully separate symmetric from asymmetric trees, and hierarchical from non-hierarchical trees.

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