We observed the use of "simple" heuristics instead of rule based decision trees (the ones usually stated in DCPGs).
We illustrate the concept of phylogeny-assisted genomic analysis using a simple tree statistic that we call phylogenetic imperfection.
They are associated to a linear array of VLIW processors which are interconnected using a simple tree network that ensures an O log(n)) data propagation time.
Use of a simple decision tree prediction model permitted stratification into four outcome prediction groups: good (prehospital ROSC and initial shockable rhythm), moderately good (prehospital ROSC and initial nonshockable rhythm), poor (prehospital non-ROSC and witnessed arrest) and very poor (prehospital non-ROSC and unwitnessed arrest).
Materials would be simple: trees of various types, gravel, stone and grass.
The immediate interpretative advantage of CTs is only obtained with simple trees [ 31, 32].
This prompted use of the simpler system.
Although as determined by the natural history of AR it could be more appropriate to use a Markov model instead of a simple decision tree, we used the latter because we considered that using a simple decision tree it was possible to include the most important clinical events resulting from each of the two therapeutic options without unrealistic simplifying assumptions.
We trained thirteen types of classifiers: complex tree, medium tree, simple tree, linear Support Vector Machines, quadratic Support Vector Machines, boosted trees, bagged trees, subspace discriminant, subspace KNN, RUSBoosted Trees, Fine KNN, Coarse KNN and random forests.
The gauge of her feelings about the city is a simple tree.
