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Moreover, classifiers usually have to rely on a large-scale dataset for training.
The proposed database is an extension of the large-scale dataset that was introduced in [22].
We train classifiers on local patches from a large-scale dataset.
In Section 5, we evaluate our solution using a large-scale dataset.
Consequently, it can be confusing for learners when selecting suitable courseware from a large-scale dataset of e-learning options.
With the growing data science trend, we always need a large-scale dataset to efficiently solve a problem.
Moreover, our proposed RNNLM-Brown converges twice faster than RNNLM-Freq, which is critical for a large-scale dataset.
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Comparison results show that ML-KELM outperformance in large scale dataset with high dimension instance feature.
However, most of the related studies mainly focus on learning features and representations from very large scale dataset relying on deep network architecture, which is doomed to fail with limited training samples due to its high complexity.
There are also quite a few research works which address some challenges in big data analytics with keywords like "huge data," "large scale dataset," and "high speed streaming data,", but no "big data".
Deep learning is a biologically-inspired variant of multi-layer perception, which supplies an end-to-end framework for feature extracting and classifier training on large scale dataset [61 63].
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