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Support vector machine (SVM) is a method for the classification of both linear and nonlinear data.
Support Vector Machines (SVM) classifier was used for classification of both datasets.
Classification of both images allowed consideration of the influence of permanent standing water.
Table 1 shows the inter-observer agreement of the quantitative BI-RADS classification of both mammographic readers.
Agreement between the classification of both readers versus the semi-automated analysis was considered only moderate with weighted kappa values of 0.367 (experienced reader) and 0.232 (inexperienced reader).
Cover data were analysed using a sum of squares agglomerative (SSA) classification (Orloći 1978) which provides a classification of both sites and species.
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There are three major classifications of both plain and rolling element bearings: radial, thrust, and guide.
In a few cases, the PA was less than 50% for the classifications of both ischemia and infarction.
MC-UPGMA and TRIBE-MCL performed exceptionally well in our comparison and nicely reconstructed most manual classifications of both chemosensory and ABC transporter genes.
Functional classifications of both CYP1A enzyme variants and steroid substrate structures were obtained allowing the delineation of global structural features for both substrate recognition and regioselectivity of oxidation.
DAPI intensity classifications of both entire nuclear volumes and of Barr body regions only confirmed the representation of all classes in the Barr body in both cell lines.
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