Sentence examples similar to greedy model from inspiring English sources

The phrase "greedy model" is correct and usable in written English
It can be used in contexts related to computer science, economics, or decision-making processes where a strategy aims for immediate benefits.
Example: "In machine learning, a greedy model often makes the best local choice at each step with the hope of finding a global optimum."
Alternatives: "selfish algorithm" or "opportunistic model"

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Here, we presented Jump3, a novel approach to GRN inference, which attempts to combine the interpretability of model-based methods with the scalability of greedy, model-free methods, thus bridging the gap between the two main classes of GRN inference approaches.

A greedy degree model inspired by greedy strategy is devised to select some items as initially determinate region.

Taken together, these results indicate that higher predictive power can be obtained when modelling this dataset by combining different single QSPR models with either greedy optimisation or model stacking.

In this contribution two ensemble modeling techniques, namely greedy optimization and model stacking, have been presented and benchmarked on a PCM dataset comprising the bioactivities of COX inhibitors on 11 mammalian cyclooxygenases (Table 1).

To this end, two ensemble modelling techniques supported by camb were explored, namely: greedy optimization and model stacking.

Two ensemble techniques were implemented, namely: greedy optimization and model stacking (MS), previously described in section "Ensemble modeling".

In particular, we compare AdOr against two baseline approaches: Greedy and static model approach.

Figure 9a shows that both Greedy and Static Model remains unaffected by the change in (gamma ).

With the greedy approach, the model follows some of the well-known queue approaches such as First In First Out.

Maupetit et al. developed PEP-FOLD [ 15, 16] algorithm that is based on Hidden Markov Model, greedy algorithm and coarse-grained force fields.

Pudimat et al. (2004) models a variety of features to assist in predicting binding sites, but selects the set of features in a greedy fashion, and models the features as nodes of a generic graphical model, causing topology selection of the graphical model to be NP-hard (Pearl, 1988).

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