Sentence examples for meaningful error from inspiring English sources

An added advantage of this approach is that it provides meaningful error bars for the model predictions.

As the initial learn rate, we always used the largest possible value that gave numerically meaningful error rates.

Compared with the conventional ML approach, the proposed DSW technique provides meaningful error reduction for all of the three GMMs consistently.

The results could predict the operating frequency of the SUTech-SR-1 as 68.2 rad/s with an error less than 0.4%, without causing any practically meaningful error in the design scheme, compared to the experimental results.

With hyperspectral data, the map accuracy is expected to increase; therefore, the sample size required for rigorous assessment of the accuracy within a meaningful error limit and confidence level may become prohibitively large.

Even when constrained to output a relatively small number of answers, list decoding permits recovery from errors well beyond the dmin/2 barrier and opens up the possibility of meaningful error correction from large amounts of noise [36].

Absolute dating by means of uranium and lead isotopes has been improved to the point that for rocks 3 billion years old geologically meaningful errors of less than ±1 million years can be obtained.

"To claim less than 1 percent have meaningful errors is kind of like trying to change an "F" to an "A" on you report card," said John Ulzheimer, president of consumer education at SmartCredit.com, who also pointed out that the study thanks the three credit bureaus for providing "numerous insights, guidance, and invaluable assistance with the implementation of the research".

We also found that the frequent errors are actually meaningful errors, and students tried to pose problems satisfying as many constraints as possible, which means they actually think about the structure to pose required problems in the assignments.

In consequence, errors from sensor-generated data, caused by malfunctioned sensors, dynamics of traffic events (e.g. the speed of a vehicle is too fast for its sensors to detect surrounding environment and gather meaningful or error-free data), and data manipulation from a malicious attacker (vehicle), were not considered in their system model.

Miyoshi et al. (2014) conducted 10,240-member LETKF with an intermediate atmospheric global circulation model (AGCM) and revealed meaningful long-range error correlations at continental scales.