Sentence examples for classification gain from inspiring English sources

Specifically, the hard decision method has 2 % correct classification gain for color-mix, while the proposed hybrid hard/soft method has 6, 6, and 22%% gains for color-photo, mono-text, and mono-photo, respectively.

Specifically, features from [1] have 2, 6, 9, 3, and 8%% higher classification accuracies for color-text, color-photo, mono-text, mono-mix, and mono-photo, respectively; while our proposed features have the correct classification gain of 1 and 19 % for color-picture and mono-picture, respectively.

Stefano Garzelli was the winner of the mountains classification, gaining points for consistent high placings on the summit stage finishes, as well as a brief breakaway on the mountainous stage 10.

Despite not winning any stages during the race, Gianni Meersman of won the green jersey, for the winner of the points classification – gained at intermediate sprints and stage finishes – while the red and white polka-dotted jersey for the King of the Mountains classification went to rider Thomas Damuseau.

This is an object of our further empirical studies what is the improvement in classification gained using this technique with greater numbers of scans in the trained set?

From Fig.  9, we can see the combination LR with FEWT acquires highest percentage classification accuracy gain with respect to the best combination of EWT with or without feature selection.

It seems that the influence of misclassifying unrelated individuals outweighs any correct classification rate gain due to improved estimates of allele frequencies due to more complete sampling.

Classification as gain or loss was based on the ADM-2 algorithm as implemented in CGH-analytics (threshold 6) and a visual inspection of the log2 ratios.

A prediction of potential functional effects of these network changes, including a classification in gain or loss of function goes beyond the scope of the BC-ratio but the provided data on RNA and protein abundances can support such interpretations.

Based upon experimental results, the classification accuracy gains are 90%% higher, in which the classification features are extracted from the combined LF&HF signal.

The Melone classification has gained more reliability and precision with the inclusion of CT scanning in the diagnostic armamentarium and is therefore commended as the classification system of choice for intra-articular fractures.