noisy data


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noisy data

(1) Corrupted electronic signals. See noise.

(2) Data that have been input erroneously or corrupted in some processing step.

(3) Unstructured data that cannot be interpreted by machines. See noisy text.
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(4) AFC data also contains some noisy data; for example, passengers may swipe in and swipe out from same stations.
Nevertheless, processing this kind of images is challenging as they include noisy data that needs to be discarded in order to help physicians make accurate decisions.
Though the BB-KDF is described for biometrics in the paper, it is applicable to other fuzzy or noisy data.
In Section 4 we briefly summarize the options to solve the ill-posed inverse problem of reconstructing the imbalance [p.sup.0] from given noisy data u.
In this paper, we introduce a hidden random variable and cluster occupants based on it and use hidden variables to deal with noisy data (e.g., clothing, metabolic rate, air velocity, etc.)
Additionally, when there exist noisy data in the training set, the classification accuracy would be greatly decreased.
Consequently, appropriate denoising and classification techniques should be explored to effectively deal with noisy data and enhance classification performance.
From the view point of instance selection [12, 17], a given training set generally contains noisy data or outliers that can degrade the final performance of a learning model.
According to Fritz, depth sensors, such as those of the Microsoft Kinect, are very powerful, but unfortunately they do not work equally well on all materials, which leads to noisy data or even missing measurements.
The preparation of data for analysis in this study included data cleaning by fill in missing values, smooth noisy data and resolve inconsistencies.
First we solve the forward problem with some specified function f(r) to simulate the final value u(r, T) := g(r) by (2.2), then we add artificial random noise to g(r) for generating the noisy data [g.sub.[delta]](r).
These challenges are not restricted to mathematical ones such as "noisy data" or poorly applied statistics in the form of--say an omitted variable bias that might have an econometric solution--but philosophical issues that had been raised by Karl Popper in the twentieth century (Thornton 2016).