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Threshold-dependent measures are likely to increase the number of true positives of the classifier at the expense of increasing in false positive, and they are often employed to access the performances of machine learning methods.
The standard classification performances of machine learning methods on three selected empirical datasets (i.e., Cypraeidae, Drosophila and Inga) are compared with respect to the ones obtained using other parameter configurations (listed in Additional file 2: Table S2, S3, S4 for Cypraeidae, Drosophila and Inga, respectively).
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These issues affect the performance of machine learning techniques, but it has little control on them.
To assess the performance of machine learning methods more accurately, we studied the upper boundary performance of the machine learning-based system on the training set after preprocessing.
The energy dissipation performance of machine augmented composite materials is investigated.
In machine learning, excessive features degrade the performance of machine learning algorithms due to over-fitting problems [17].
IBM is working with the U.S. Air Force to improve its TrueNorth line of chips designed to optimize the performance of machine learning models at the hardware level.
Due to the increase in performance of machine parts, the surfaces of l00Cr6 ball bearings are highly stressed.
Single image haze removal is a challenging task with few effective constraints, which seriously affect performance of machine learning algorithms.
The paper presents a thorough analysis of the influence of the number of negative training examples on the performance of machine learning methods.
The aim of our study was to examine the impact of such fingerprint density on the performance of machine learning methods.
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