Your English writing platform
Discover LudwigExact(50)
Dense screen loading improved the classification rates, but hampered flowability, and consequently the efficiency.
We found that using the catalogs with missing data improved the classification performance by 15% in efficiency and by 8% when comparing to traditional missing data approaches while the computational cost remains the same.
Jointly training of two DNNs with the proposed cost function improved the classification accuracies and minimized the over-fitting effect for both speech-based and image-based systems.
A comparison between the results obtained by this approach and by other standard initialization methods showed that our algorithm clearly improved the classification ability of RBFN.
We show how we improved the classification components and feature selection in order to achieve high-level throughput and high-level accuracy for the classification task of big data.
We found that continuum-removal improved the classification performance of most endmember-models, although a larger portion of pixels remained unmodeled with the CR spectra (2%) compared to the non-CR spectra (0%).
Similar(10)
This again can improve the classification speed.
So the LSROPC can help improve the classification accuracy effectively.
In a series of experiments, we show that spaced seeds consistently improve the classification accuracy.
The inclusion of texture information in the classification did not improve the classification quality.
Experimental results show that the proposed algorithm improves the classification performance in monotone classification tasks.
Write better and faster with AI suggestions while staying true to your unique style.
Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com