Sentence examples for preprocessing effort from inspiring English sources

While the total time shows the cumulative preprocessing effort, the maximum time shows the latency when we use parallel preprocessing on multiple cores.

Our empirical results on data obtained in a couple of existing studies demonstrate that one of our algorithms stands out as a strong candidate providing microsecond online response time and competitive influence spread, with reasonable preprocessing effort.

It is our hope that recent augmentation of open science data-sharing initiatives with preprocessing efforts will catalyze the involvement of these researchers by reducing common barriers of entry.

The comparison among BEM and other numerical approaches proceeds through the evaluation of inherent accuracy, preprocessing and postprocessing efforts, and run times.

This paper presents our further efforts in developing image preprocessing algorithms and dealing with angled facial images.

Hence, we focus our efforts on context-based data preprocessing.

Discovering knowledge in such complex, high-dimensional datasets needs a concerted effort of various topics, ranging from data preprocessing, data fusion, data integration, and data mapping to interactive visualization within a low-dimensional space [41].

As future lines of work, we are going to focus our effort on reducing the computational cost of the preprocessing step when applying subsampling, which is known to produce different results because samples are not removed in the same order each time they are applied.

We made an effort to preprocess this yeast microarray data set and examined the validity of the presented results from the perspectives of S. pombe probes sets, non-normalized expression profiles, and other considerations and supporting examples reported in our previous work.

In (Huang et al. 2015) several data preprocessing techniques were empirically assessed on the effectiveness of machine learning methods for effort estimation.

The main motivation of these studies is to achieve an unbiased criterion when comparing different software estimation models and evaluate the effectiveness of data preprocessing techniques, data attribute selectors, and machine learning algorithms in the context of software effort estimation.