Nevertheless, we think that affects combined with other features extracted from tweets, location-specific feature selection (e.g., our findings from cross-correlation analysis), as well as deep neural network models for sequence prediction can boost prediction accuracy, and even forecast ILI dynamics several weeks in advance.
Our method boosts prediction performance of hot spots by using unlabeled data to overcome the deficiency of available training data.
In this article, we consider a variety of learning strategies to boost prediction performance based on the use of all available data.
Therefore, describing genetic background using markers can potentially boost prediction accuracy above and beyond what can be achieved using family history.
Models that incorporate both this information and FOBT results should be developed and evaluated as this may boost prediction still further.
To achieve network denoising, which boosts prediction performance, we first applied minimum curvilinear embedding (MCE), and then adopted shortest path (SP) in the reduced space to assign likelihood scores to candidate interactions.
Moreover, recently emerged deep neural networks for sequence prediction, e.g., Long Short-Term Memory models, can potentially boost predictions when learned jointly on text and affect signals.
This fact shows that MNet based on Y ˜ can more accurately predict the labels with few member proteins than MNet based on Y, and explicitly considering the unbalanced problem in data integration based protein function prediction can boost the prediction accuracy.
(ii) Boost prediction accuracy via applying various regression algorithms.
Second, a cascaded prediction strategy is designed to utilize the predicted spectral features as auxiliary input to boost the prediction accuracy of the excitation features.
