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(Or it requires machine classification).
In general, there is a reasonable coherence between the human and machine classification.
Table 1 provides machine classification performance results for four different protected characteristics.
This builds on previous work that developed a machine classification system for religious cyber hate [1].
Table 5 shows classifier output with expected class on the vertical axis and machine classification result on the horizontal.
In this paper we developed novel machine classification models to identify different types of cyber hate individually and intersectionally.
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Changes in land use/land cover are estimated from support vector machine classifications of Landsat mosaics with grey-level co-occurrence matrix features.
The techniques included logistic regression, Bayes network, Chi-squared Automatic Interaction Detection (CHAID), neural net, support vector machines, Classification And Regression Trees (CART) and "decision list" models.
Results of predictions and confidence for the average of duplicate arrays for cases of CIRS-ciguatera and controls using a Support Vector Machines classification algorithm, while withholding four data profiles* from training.
Their experimental results showed that machine learning classification combined with MFS significantly improved the performance of binary classification.
PSS was combined with the Support Vector Machine (SVM) classification.
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