Its principal objective is to improve classification accuracy of fuzzy models.
Compared to the original non-linear activation, our proposed method can improve classification accuracy without increasing computation cost.
Extensive research and evaluations have been conducted on neural networks to improve classification accuracy and training time.
In comparison to traditional monolithic neural networks, both the proposed architectures improve classification accuracy and minimize the training complexity.
For each classification algorithm, the inclusion of the GWPCA loadings data was found to significantly improve classification accuracy.
An ensemble of classifiers has been proved to be an effective way to improve classification accuracy in difficult pattern recognition environments.
Since metabolomic data can be obtained from different samples or analytical techniques, data fusion has been applied to improve classification accuracy.
Unfortunately, despite extensive research to improve classification accuracy, BCIs almost always exhibit errors, which are sometimes so severe that effective communication is impossible.
It also includes intra- and intercognitive computing models to connect STE-M and random forest models and improve classification accuracy with spatial and temporal reasoning.
The results of this study can aid in the design of various hybrid techniques that combine the strengths of different methods to improve classification accuracy and reliability.
Trying to improve classification accuracy of M-NSA we propose a new version of this algorithm, called MINSA, where refinement of receptors set is applied.
