Sentence examples for model our algorithm from inspiring English sources

Given a social network as well as an influence propagation model, our algorithm iteratively finds n−1 partitions and selects the one with the maximum value as the final output.

Therefore, to model our algorithm's sensitivity to picking errors, we add a set of random picking errors to the synthetic arrival times at each receiver, ranging from 0 to 5%% of the calculated travel time.

On the three-cell model, our algorithm needs 33 seconds and the direct algorithm 231 seconds.

Even if the structures do not follow any statistical model, our algorithm outputs accurate results.

Using a consensus approach to combine the results from logistic regression and GENN models, our algorithm was successful in differentiating between high- and low-risk groups (sensitivity 77.0%, specificity 74.1%).

In this paper, we focus on the host/parasite associations but we want to call attention to the fact that, due to the similarity of the models, our algorithm can be straightforwardly applied to the other problems as well.

We model our CGP algorithm after the one in [26], which operates on transmit filter matrices T u instead of on the covariance matrices Q u directly.

This type of distortion can be modeled by a projective transformation, and we use the affine model in our algorithm as a particular case of the projective one[38].

We have modelled interactions with age and included these in the final model, so our algorithm takes account of the differential effect of three key variables (family history of diabetes, body mass index, and smoking) at different ages.

We examine separately how network models and energy models affect our algorithm performance.

Comparing with the classical explicit schemes presented in [22] which strongly depends on the regularization methods and the model parameters, our algorithm controls the problem of stability and is significantly faster.