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As such, rather than just confining binary matching method to pure Hamming distance calculation, these extensions significantly broaden the practicality of performing binary matching and enable a strong performance resemblance of a powerful classifier such as a multilayer perceptron (MLP) [21] or a SVM [22] when the bits allocation to each dimension is substantially large.
Since we are performing binary sentiment classification, the final fully-connected layer contains two neurons using the softmax logistic function [19].
When performing binary classification, the prediction can be transformed to continuous values between 0 and 1 (this is e.g. the ratio how often a class was predicted as active).
Although the automated classifier is based on a particular machine learning methodology, results are likely to be reflective of the potential benefits of any highly performing binary classification tool and therefore provide insights into the general impact of CADx on (123I FP-CIT reporting.
We estimated relative risks as ORs by performing binary logistic regression with pre-eclampsia as the outcome and organic food consumption as the exposure.
Relative risk was estimated as ORs by performing binary logistic regression with pre-eclampsia as the outcome and organic food consumption as the exposure.
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All the others perform binary classification.
The goal is then to perform binary classification.
A number of techniques have been proposed to perform binary classification.
We introduce a novel method referred to as adaptive mean modulation (AMM) to perform binary embedding of packaged information.
The majority of the existing cheminformatics methods for predicting drug target interactions perform binary classification into binding and non-binding.
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