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For CCPs problem, the experimental results demonstrate that the proposed method outperforms several traditional methods.
Additionally, our method outperforms several other recent competitive solvers for the two challenging case studies considered.
Numerical simulations showed that the proposed method outperforms several well-known blind equalization schemes and works well even in fast-fading scenarios.
Experiments on real world data show all the information used can help improve the recommendation performance, and our final method outperforms several baseline methods.
The method outperforms several commercial RNA amplification kits and can be used in conjunction with a variety of downstream microarray platforms (cDNA arrays, oligonucleotide arrays, Affymetrix GeneChip™ assays).
In a number of experiments using both synthetic data and real neuronal patterns of different model organisms, including fruit fly, C.elegans and mouse, our method outperforms several comparison methods in terms of accuracy, consistency, robustness and speed.
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In our experiments, the GD method outperformed several comparison methods in reconstruction accuracy, consistency, robustness and speed.
We show that these methods outperform several other published clustering methods in terms of correctly predicting a classifier and especially in terms of the correctness of the provided prediction.
The visual results demonstrate that the proposed method outperforms in several cases compared with some state-of-the-art classical methods (Figs. 9 and 10).
The algorithm outperforms several methods currently used, providing considerably better accuracy and it is suitable for validation of known splicing events as well as for exploration and discovery of novel non-annotated transcripts in human samples.
Experimental results show that the proposed tracking method successfully tracks the objects in different video sequences and outperforms several conventional methods.
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