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For example, gossip based aggregation (Jelasity et al. 2005) calculates the average function as an iterative variance reduction algorithm over the values of nodes in an overlay network.
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For synchronous changes, the performance of the two aggregation methodologies, i.e., number of messages and convergence speed, becomes comparable as the iterative variance reduction algorithm requires recomputation of the aggregates.
We can interpret this as an iterative quadratic regularization inversion followed by the estimates of variances τ j which are used in the next iteration to define the variance matrix D. This algorithm is simple to implement.
We can interpret this as an iterative quadratic regularization inversion followed by the estimation of variances τ j which are used in the next iteration to define the variance matrix D.
Algorithmically, PageRank is easiest to describe as an iterative process.
The analysis developed as an iterative, cyclical, and reflexive process.
Data collection and analysis proceeded as an iterative process.
Collaboration can be seen as an iterative process.
As an iterative procedure, the EM algorithm requires initial values.
We see this as an iterative approach to consent, also known as circular consent [ 5].
These procedures were not specified a priori and were therefore conducted as an iterative process.
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