Sentence examples for be learned by using from inspiring English sources

The nonlinear approximation can be learned by using machine learning approaches, e.g., Gaussian Process (GP) regression, which is suitable for high-dimensional and small-sample nonlinear regression problems.

"While there is much that can be learned by using existing stem cell lines if they are made available for research," the report said, those cell lines might change over time.

Furthermore, they also showcase that much of CEACAM functionality can be learned by using CEACAM-binding bacteria, such as N. gonorrhoeae, as selective and potent stimuli.

We consider that improvements in academic infrastructure are sorely needed to facilitate cross-disciplinary translational studies that can someday connect what can be learned by using model organisms with real-time samples from patients.

This was not reflected in Simulation 1. Simulation 2 explored the effect of allowing the orthographic representations to be learned by using the same architecture as Simulation 1, but without applying orthographic targets to the orthographic layer of units.

Moreover, there are many examples in which new knowledge about cell therapy can only be learned by using direct data from human cells, and tests or trials on model organisms (such as mouse or rat) can not elucidate the specific molecular signature of human cells [ 80].

Since LPP supports exact out-of-sample extension, the matrix A could also be learnt by using the training data alone and then be applied on any new data set.

The joint trajectories of this reference gait are learned by using neural networks.

Here, the HMM parameters were learned by using the hybrid approach suggested by Jax and Vary [13], and the state number of HMM and mixture number for each GMM were also selected in terms of the estimation error of the HF spectral envelope.

These networks allow combinations of features or inputs occurring in a given spatial arrangement to be learned by neurons using competitive learning (Rolls 2008 ), ensuring that higher-order spatial properties of the input stimuli are represented in the network.

In general, the classifier function is not known a priori and has to be learned by training the classifier using examples (training data).