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The implementation is based on a partially decentralized system architecture and statistical marginalization, and sampling-based measurement updates.
The implementation is based on the DSR module.
The implementation is based on the divide-and-conquer principle.
The implementation is based on the MC-SVM formulation of Crammer and Singer [30].
The implementation is based on several open-source libraries supported on major platforms.
The implementation is based on using memory and heap data structure for symbol vector sorting.
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The implementation was based on the MVVM (Model, View a ViewModel) architectural pattern which is briefly described below.
The implementation was based on a fully connected test assignment, i.e., each node tests all others.
The evaluation of the extent of the implementation was based on a four stage model of success in simulation studies [ 24].
Availability and implementation: The implementation was based on the existing Gaussian process latent variable model toolbox (https://github.com/SheffieldML/GPmat); extensions for noise models and kernels accounting for censoring are available at http://icb.helmholtz-muenchen.de/censgplvm.
The implementations are based on the open source toolkits, CSLM [17] and RNNLM [18], to ensure the repeatability.
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