Sentence examples for a comparative classification of from inspiring English sources

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We construct a comparative classification of competing demands using these characteristics as our distinguishing features.

In a comparative classification of alpine mosses, lichens and seed plants, strong illumination caused photodamage in dried leaves, but not in dry moss (Grimmia alpestris) and dry lichens [ 63].

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For comparative classification of the T3SS effectors, the Pseudomonas syringae Hop database http://pseudomonas-syringae.org/Hop_database.xls was blasted on protein level versus the annotation of the E. pyrifoliae DSM 12163 T genome.

Secondary outcomes : Relapse rates, quality of life, adverse events or acceptability of the technology (by patient, carer or health professional), histological confirmation via lesional biopsy, or independent imaging or comparative classification of staging and risk classification of disease and treatment alteration in the light of imaging tests performed.

Comparative classifications of LTR-RTEs from closely related species were performed on monocots in which transposable elements (TEs) had previously been well characterized [ 15, 16], however this is the first time this approach was employed in forest trees.

For HHPSO, the hierarchical population structure consisted of five layers as shown in Fig. 2. The comparative classification results of the five different algorithms are summarized in Tables 1 (Stage I) and 3 (Stage II).

A comparative study of classification accuracy between Relevance Vector Machine and Support Vector Machine (SVM) classification is also presented.

A comparative illustration of classification accuracies, achieved by the operators at different noise level, is provided in Table 4.

Abu-Nimeh et al. [11] presented a comparative evaluation of classification techniques such as Logistic Regression, Bayesian Adaptive Regression Trees, Support Vector Machines, Random Forests, and Neural Network.

We plan to extend the range of comparative classification algorithms in subsequent work to more advanced classifiers such as SVMs, tree-based methods, boosting, and relatively new ones that have been gaining recognition.

Li et al. have performed a comparative study of multiclass classification methods for tissue classification based on gene expression (Li et al., 2004).

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