Sentence examples for task method from inspiring English sources
The objective of the synthesis phase of the Scientific Method is to implement the task method to accomplish the goals and validate the hypotheses of the task.
The task method comprises the solution specified to achieve the task objective and the experiments designed to determine the effectiveness of the solution.
The analysis is done at three levels: task, method and domain knowledge, distinguishing the generic components in most of the IU tasks, thereby enabling the components to be reused.
Table 1 Normalized average of spectral subtraction and ANS spectrum errors (RMSE criteria) on test set A of Aurora 2 task Method Average of spectrum error 20 dB 15 dB 10 dB 5 dB 0 dB −5 dB SS 332 378 488 653 982 1500 ANS 22.4 28.1429.3 55.9 87.9 142.
No statistical test is conducted due to unavailability of the per-query result of SNUMedinfo Table 7 Comparison to WSU-IR, the best automatic run in the TREC 2015 CDS task Method infNDCG infAP WSU-IR 0.2939 0.0842 (BM25+SEM_{D^k_{PRF}}{PRF}}) 0.3016 2.62% 0.0873 3.68% Results of WSU-IR are taken from those reported in [2].
The Task method used the estimated impulse response functions, which contain variations in the hemodynamic signal over time, as reproduced in all blocks.
Table 5 Results of image classification task Methods mAP VLAD 0.5601 CEVLAD [31] 0.5665 EEVLAD [31] 0.5714 Improved VLAD 0.5580 SDEVLAD 0.5804.
This phenomenon has been investigated using three broad methods: correlational methods, simulator methods, and cognitive task methods.
Secondary task methods of measuring cognitive workload involve the addition of a so-called secondary task, performance on which varies depending upon the hypothesized 'spare capacity' remaining for the user.
The baseline method is designated as method 1, the multi-task method is method 2, and the multi-domain and multi-task approach is designated as method 3.
In order to find the best performing multi-task method alongside the best performing learning algorithm, we attempt to find the best performing feature/multi-task algorithm/learner combination with their different parameters.
