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In addition, the efficiency of our method would not decrease for large benchmarks with thousands of gates.
For experimentation purpose, ten large benchmarks are generated, synthesized, and partitioned using a combination of locally developed and commercial tools.
We assume that such large benchmarks of 1000 queries guarantee the significance of our results (28).
Experimental results confirm that our method generates significantly better alignments and threading results than the best profile-based methods on several large benchmarks.
Our local version of the GOA database had two goals: to provide large benchmarks of abstracts and relevance judgements for the evaluation, and to provide the needed knowledge base for the machine learning classifier.
Experimental results show that our context-specific alignment potential is much more sensitive than the widely used context-independent or profile-based (which is position specific) scoring function, generating significantly better alignments and threading results than the best profile-based methods on several large benchmarks.
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Using a large benchmark the effectiveness of the new formulation is demonstrated.
The SVM training is the step which needs maximum time for execution among all components of system as it trains large benchmarked data set mnist with 60,000 images.
Such calculation should be derived from the comparison of existing methods in a large benchmark set of sequences, as it was the case in this study.
This gave us the possibility of performing large benchmark calculations, with a Full-CI space dimension up to almost ten billion of symmetry-adapted Slater determinants.
These new approximation algorithms are exhibited on some of the small, intermediate and large benchmark graphs and the results are compared.
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