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The Open Proposals approach allows us to gain the best features of bottom-up approaches to innovation while still allowing iterative improvement, fluid team formation, and full integration with existing programs and personnel.
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This article presents a survey of these proposals (approaches).
The proposal approach was validated on several examples using logical constraint programming techniques.
In this paper we discuss a variation of the multiple-proposal approach to Markov chain-based Monte Carlo methods.
In addition, the results also depicted that the proposal approach can well address linguistic inputs in group decision making processes.
Our experiments proved the efficiency of our proposal approach in comparison to some previous research developed in this area.
So, the performances of the SLNR precoding for suppressing the intra-group interferences and our proposal approach to that of the generalized MRT when G L=256.
To evaluate the effectiveness of our proposal approach, classification is carried out using a K-Nearest-Neighbors (K-NN) classifier using a database of 121 writers.
The classical grouping-based object proposal approach can produce region proposals with high localization accuracy, but incorporates significant redundancy for the lack of object confidence to evaluate the proposals.
We tested for rapid radiation during early fungal evolution using the Bayesian polytomy proposal approach suggested by Lewis et al. (2005) and implemented in P4 (Foster 2004).
However, in the sticky MCMC algorithms (with update rules as R1 and R3 in Table 2), the normalizing constant of the adaptive non-parametric proposal approaches the normalizing constant of the target.
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