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Subsequently, the screening and selection of above models is initially based on profile-based elimination of incompetent schemes followed by non-linear regression search performed using the Levenberg Marquardt algorithm (LMA) for the chosen models.
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Prediction models were initially generated using ANNE and RF.
In the 1980s, some deterministic toxicant-population models were initially proposed by Hallam [16, 17].
The bidirectional associative memory (BAM) neural network models were initially coined by Kosko, see [1, 2].
Stochastic Frontier Analysis SFAA) models were initially introduced by Aigner, Lovell and Schmidt (1977) and Meeusen and Broeck (1977).
Nevertheless, these models are initially conceived for wired network topologies, and based on the static network.
Adaptive learning models are initially trained to correlate operating parameters or product composition with performance.
Multiscale process models are initially developed, linking continuum conservation laws with microscopic scale simulators.
In particular, the GARCH 1, 1) and APARCH 1, 1) models were initially estimated with normal error distribution assumption.
To further support engineers, new models are initially marked as drafts and their visibility limited to the model owner and any contributors explicitly invited.
Two CRF models were initially learned from the CHEMDNER training corpus: one with only the default features and another with our engineered ones.
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