Sentence examples for classification of success from inspiring English sources

An accurate and reproducible measurement method for joint motion is essential for classification of success or failure in therapeutic intervention.

At the end a dichotomous variable should result for two reasons: a) facilitating clinical decision making in the future and b) definite classification of success and non-success for prognostic analysis The algorithm resulting in a dichotomous success variable (yes/no) should be as easy as possible to integrate five different constructs.

The mixed-effects engine explores logistic regression for classification of virological success and multiple linear regression for regression of actual VL change.

Section 4 provides evaluation of the algorithm in terms of classification success and comparison to other approaches, and is followed by a conclusion (Section 5).

An application of both techniques is evaluated on the same case study, giving special emphasis to their performance in terms of classification success and computation time.

We highlight the supremacy of the proposed methods, compared both to the typical audio-based and deep-learning methods that adopt handcrafted features, and we evaluate our system in terms of classification success and run-time execution.

With this in mind our primary concern before deciding to use the 34plex as the first choice system over uni-parental loci was the estimation of classification success.

We found no distinct difference between the two approaches in terms of classification success rates (data not shown).

'Resubstitution' estimates of classification success can be computed by classifying the same cases used to create the classifier, but the estimates are biased and often highly overly optimistic.

For example, EEG patterns can successfully predict whether an object is an animal or a tool within 200 ms (Simanova et al. 2010), although it is unclear if the basis of classification success is visual or semantic (or both).

Fig. 8 The probability of correct classification (average success rate) vs. number of hidden neurons by using different wavelet transforms and various input size in an AWGN channel.