Sentence examples for classification benchmark from inspiring English sources

Our results on a wide range of commonly used DT classification benchmark datasets prove the robustness of our approach.

Experimental results on several classification benchmark problems show that the SFC-SVHC achieves good classification results with a small number of rules.

Over 3000 new classification tasks were added to our recently established protein classification benchmark collection that currently includes protein sequence (including protein domains and entire proteins), protein structure and reading frame DNA sequence data.

The performance of the proposed method on ten classification benchmark datasets shows that the average of accuracy of this method improves 1.94%, 3.7%, and 3.74% compared with the mixture of negatively correlated experts, ME and the negative correlation learning, respectively.

Quantitative evaluation shows that the proposed algorithm outperforms general object classification algorithms significantly on standard HEp-2 cell patterns classifying benchmark1 and also achieves competitive performance on standard natural image classification benchmark.

Finally, to verify the efficacy and feasibility of the proposed algorithms, i.e. GSI-ELM and IGSI-ELM, in this paper, experiments on regression and classification benchmark data sets are investigated.

The method is tested using well known classification benchmarks.

Finally, the method is compared against other classifiers on several image classification benchmarks.

A semi-supervised setting is demonstrated achieving state-of-the-art results on the MNIST classification benchmarks.

While designed for imitation and RL, our work is more generally applicable and also advanced the state of the art in standard few-shot classification benchmarks such as omniglot and mini-imagenet.

Currently, many practical approaches are available in the literature for the application of thermoeconomic analysis and Exergy Cost Theory to energy conversion systems, while a comprehensive classification, benchmarking and comparison of such approaches is missing.