Sentence examples for classifications simultaneously from inspiring English sources

We propose a novel and general framework called the multithreading cascade of Speeded Up Robust Features (McSURF), which is capable of processing multiple classifications simultaneously and accurately.

To overcome the limitations of unsupervised and supervised methods, we propose a new method, which utilizes the class labels to a less important role so as to perform class discovery and classification simultaneously.

Saon et al. [28] proposed a method to train a single network that conducts speaker adaptation and phone classification simultaneously by feeding I-Vectors (speaker identity features) to the network.

Similar to wrapper methods, embedded methods attempt to perform feature selection and classification simultaneously.

Since the inclusion of covariates changed the number of cases in each class, we found it was necessary to conduct classification simultaneously with class membership predictions [ 42].

Our auxiliary task shares the first m layers of the original MLP, but have a new output layer: (5) This network is trained to distinguish partial positive examples from negative examples (e.g. classification), simultaneously as we train the original network on labeled data.

In antibody-positive patients, 6 presented a grade II/III and 2 patients a grade I interstitial fibrosis and tubular atrophy according to Banff'07 classification; simultaneously 5 patients had signs of chronic active antibody-mediated rejection without C4d deposition (peritubular capillaries and/or glomerular inflammation) and 4 patients presented with mild to moderate interstitial infiltration.

However, no important differences in results were observed when multivariate models were run using different hierarchical classifications or simultaneously including all the factors in the multivariate model.

Our results demonstrate a powerful new tool for identifying cortical subnetworks by objective classification of simultaneously recorded electrophysiological activity.

The BGP integrates certain types of data pre-processing and post-processing methods with conventional GP engine to enhance its ability to solve both regression and classification problems simultaneously.

Because of replacing single objective criterion with multi-objective criteria and adopting uniform design to seek experimental points that uniformly scatter on whole experimental domain, MOUD can reduce the computational cost and improve the classification ability simultaneously.