Sentence examples for based models can achieve from inspiring English sources

Consequently, while SVM and K-nearest neighbor show overestimation in predicting cloud and snow/ice pixels, our Random Forest (RF) based models can achieve higher cloud, snow/ice kappa values on MODIS and thin cloud, thick cloud and snow/ice kappa values on Landsat 8 images.

Compared with the concept cooccurrence and grammar engineering-based approaches like SemRep, machine learning based AB and BC models can achieve better performance in mining association between bioentities from texts.

Several VW car models can achieve 50 highway m.p.g., whipping their 40- to 43-m.p.g.

Physical models can achieve high predictive accuracy if appropriately built.

Compared with the concept cooccurrence and grammar engineering-based approaches like SemRep, machine learning based models usually can achieve better performance in information extraction (IE) from texts [ 14].

Compared with the concept cooccurrence and grammar engineering-based approaches like SemRep, machine learning based models usually can achieve better performance in information extraction (IE) from texts.

The reason why our method can discover more effective linking terms than SemRep is that, compared with grammar engineering-based approaches like SemRep, machine learning based models usually can achieve better performance in information extraction from texts.

The study demonstrates that our portable PIC implementation based on the OpenACC programming model can achieve performance closely matching theoretical predictions.

These results suggest that the conditional GCM approach based on a linear multivariate vector autoregressive model can achieve greater accuracy in detecting network connectivity than the widely used pair-wise GCM, and this group analysis methodology can be quite useful to extend the information obtainable in fMRI.

Experimental results show that this integrated model can achieve even higher F-score solely based on the training corpus for gene mention tagging.

Traditional epidemiologic models choose to simplify reality by ignoring both the spatial distribution of populations and possible intrapopulation heterogeneities, whereas more recent solutions based on Individual-Based Modeling (IBM) can achieve high precision but are costly to compute and analyze.