Sentence examples for many input trees from inspiring English sources

This threshold helps to prevent a core cluster being exhibited by only a small number of input trees blocking the addition of an ambiguous cluster that is exhibited by many input trees later on.

The supertree approach seeks to amalgamate the information from many input trees and therefore is a sequence-based method that is more democratic in terms of getting its information.

The parameter that seems to have the biggest impact on the run time is the total multiplicity d of those labels that appear with multiplicity greater than 1 in many input trees.

If two out of three of the conditions (g, m, or b) are favourable (i.e. many input trees, few missing taxa, and the probability that the input trees are congruent with the principal trees is high) then both methods work well.

The basic idea for our algorithm, that is, breaking the input trees into clusters and then combining some of these clusters to form a consensus tree, seems to yield good results if the input trees are not too unresolved and there are enough clusters that are exhibited by many input trees.

Since the datasets consist of many input attributes, decision tree (DT) was used to unravel the attributes that contribute towards the diagnosis.

Two alternative supertree analyses, representing the largest family-level phylogenetic analysis of Odonatoidea to date, converge on a very similar topology, with overall positive support, indicating general agreement between input trees regarding many relationships within the tree.

If the input trees have low coverage, many quartets asked by the algorithm do not appear in any of the trees, which may harm speed and accuracy.

One problem with using our algorithm for amalgamating trees on different taxa sets is that many quartets needed by our algorithm are not contained in all the input trees.

The essential approach in many tree comparison algorithms is a recursive rooted comparison of subtrees of the input trees, and finding the best combination of such sub-instance solutions to yield a solution for the input instance.

There are many consensus methods available [ 17], some of which are based on the split sets of the input trees: Strict consensus The strict consensus contains all splits present in all input trees, i.e.e.e