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To train a SOM from such a large collection of compounds, a specially tailored algorithm was developed.
The idea that serves as the basis for the following sections is to employ dynamic programming to obtain a highly tailored algorithm to facilitate efficient solution generation.
And, then, we design a specially tailored algorithm centred on getting rid of those unpopular products based on item relevance models.
Those are obtained via a tailored algorithm that outperforms two well-known generic software for global optimization.
Here, we carry out a comparative study of several MH algorithms (iterated local search (ILS) methods, variable neighborhood search (VNS) methods, genetic algorithms (GAs) and estimation of distribution algorithms (EDAs)) and a tailored algorithm A* to address parameter estimation in GMMs.
SuperLearner allows us to use a large library of candidate regression algorithms, parametric of data-adaptive, to honestly evaluate their prediction performance, and to build a new, tailored algorithm that is a combination of the best candidates.
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In order to describe the processes and their interaction accurately, tailored algorithms are required for prediction and analysis.
This can be achieved by tailoring Algorithm 1 in Bretz et al. (2009) to the graphical weighting strategies as follows.
Tailoring algorithms consider demographical characteristics and psychosocial constructs to generate personalized messages.
The web-based, computer-tailored intervention is developed using the TailorBuilder software (OSE, the Netherlands), which is suitable for generating tailored feedback based on assessment questionnaires, tailoring algorithms and feedback messages.
Tailoring algorithms that combine the responses of a person on specific questions with concurrent feedback messages generate highly personalized advice to the respondent which they receive on their screen and that can also be printed.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com