Sentence examples for learnt data from inspiring English sources

A variation of semi-supervised learning in data mining, active learning methods could also be applicable towards obtaining improved data representations where input from crowdsourcing or human experts can be used to obtain labels for some data samples which can then be used to better tune and improve the learnt data representations.

CART can be used for prediction: as an example, CART can help predict levels of HIV/AIDS prevalence rates based on previously learnt data.

More specifically, since the learnt complex data representations contain semantic and relational information instead of just raw bit data, they can directly be used for semantic indexing when each data point (for example a given text document) is presented by a vector representation, allowing for a vector-based comparison which is more efficient than comparing instances based directly on raw data.

A system learnt using data obtained from a particular region can be expected to be more precise in performing mass screening in that same region.

Vector λ corresponds to the feature weights assigned to the set of features, and is learnt from data to decide which features may be more important in predicting TFBSs.

Although capturing the dependencies of LC-MS/MS experiments, DiriSim remains a robust, non-complex model since it only needs three parameters that are to be learnt from data.

Our data do not show that medication selectively improves generalization only for the best stimuli but rather that it affects the ability to generalize learnt information overall.

It also provides feasibility to incorporate prior information learnt from undersampled data or another contrast image, which leads to optimized sparse representation of images to be reconstructed.

In the following section, we describe how weak poses are estimated from video frame feature descriptors using GPR. Figure 4 Visualizing the nine principal variations of the pose within UaSpace learnt from HumanEva data.

This will be narrowed, as lessons are learnt and early data are analysed.

The learning curve has a shallow minimum of the test error with a fractional test error of 0.1380 at m = 1563, against a final test error of 0.1490 at m = 2000, having learnt all the data in the training set.