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Boosting neural networks' accuracy with ensemble approach revolutionizes classification performance.
The article discusses ways to make neural networks better at classifying information. By using cross-validation, the researchers found that they could improve the network's performance by adjusting its parameters and structure. They also discovered that combining multiple similar networks into ensembles can further reduce errors in predicting outcomes.
