Sentence examples for model where data from inspiring English sources

Message passing specification implements a data push model, where data is always written.

The model has been formulated as a multi objective linear programming model where data are modeled by triangular fuzzy numbers.

By exploiting additional information able to improve classical inductive learning we propose a prediction model where data is naturally separated into several structured groups clustered by the size and annual turnover of the firms.

Peidro et al. (2009) proposed a fuzzy mixed integer linear programming model where data are ill known and modeled by triangular fuzzy numbers for supply chain planning under supply, process, and demand uncertainties.

Trading data as a commodity has become increasingly popular in recent years, and data marketplaces have emerged as a new business model where data from a variety of sources can be collected, aggregated, processed, enriched, bought, and sold.

The great fear, for Netflix, is that cable carriers will begin to charge per-gigabyte--a shift from the current model where data is unlimited up to a large cap.

This unique application is a model example, where data across compounds and trials established efficacy in a subpopulation and allowed patients with a high unmet medical need to receive an approved drug >2 years earlier than the traditional need for a double-blind, placebo controlled study prior to approval.

Models in which both ancillary and absorption variables were included, had the highest predictive capabilities (Radj2= 0.49 0.74 and lowest RMSE values) compared to models where data sources were derived from only one of the two groups.

The AN approach consisted of two programming models: i) the capsule model, where the data packets contained not only the data to be communicated but also in-band instructions to execute; and the ii) programmable switch model, in which the out-of-band mechanisms were utilized to execute code at various nodes[18, 69].

Scaling/transformation of the response variables (associated bioactivities) can be performed to standardize highly varying values in raw data for proper training of predictive model, where often data is transformed with a logarithmic function.

For High, it's this context that'll make possible training models with less data, as well as recent advances in transfer learning, that is, the ability to take one trained model and then use this data to kickstart the training of another model where less data may exist.