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The visual clustering algorithms were applied to the same datasets using the same feature set as described in Sect.
Using the same feature learning framework, Tang and Liu [15] also propose an algorithm to learn dense features from the d-smallest eigenvectors of the normalized graph Laplacian.
On the basis that the same principle can be applied to feature selection, we describe two approaches: (i) homogeneous, i.e., using the same feature selection method with different training data and distributing the dataset over several nodes; and (ii) heterogeneous, i.e., using different feature selection methods with the same training data.
Others use the ratio of within-cluster similarity to between-cluster similarity, with higher ratios denoting a better cluster solution [45], often times using the same feature upon which the clustering was based.
I observed Sylvilagus cf. audubonii in the Southwest using the same feature of reedbeds (Fig. 5).
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This is the same as the optimization problem of Equation (20) with fixed bases b k and can be solved using the same feature-sign search algorithm.
The SSVM-based CEM systems outperformed the CRF-based CEM systems when using the same features.
The dataset is then visualized with CheS-Mapper, using the same features for embedding that were used to validate the (Q SAR model.
Our experiments on the CHEMDNER challenge corpus show that the SSVM-based systems outperformed the CRF-based systems when using the same features.
We proposed a version of SimBoost, called SimBoostQuant, which, using the same features, predicts binding affinities as well as prediction intervals as a measure of the confidence of the prediction.
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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