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Based on Leydesdorff's (2003) method for label assignment to addresses, we coded a Pascal CDS/ISISd programme that assigned each address the label 'university' (abbreviated UNIV), 'industry' (abbreviated INDU) or 'government' (abbreviated GOV) to each address.
Each label assignment in the pool is first evaluated as an output candidate, then selected with uncertainty for privacy consideration.
Through minimizing the overall loss on both labeled and unlabeled data, we generate a label assignment pool.
This procedure was conducted 150 times per participant for object perception (encoding) and retrieval, respectively, with a new random trial split and random label assignment on each repetition.
The maximum likelihood method of label assignment is modified by being multiplied with a function, called utility function, that expresses the information coming from the logic rules programmed to the system.
Using standard relational tables to capture CRF parameters, and inverted-file representations of text, we show that the Viterbi algorithm for CRF inference can be specified declaratively in recursive SQL, in a manner that can both choose likely segmentations, and provide detailed marginal distributions for label assignment.
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Based on real-world social networks as well as random graphs, we provide numerical results showing signi€cant improvement compared to random selection of anchor nodes and label assignments.
Rewarding appropriate label assignments.
Penalizing inappropriate label assignments.
● Random (uniform) label assignments have an ARI score close to 0.0, thanks to the 'corrected-for-chance' adjustment made in order to reduce the effect of random labellings.
As evaluation measures we report in Table 1 the results obtained using a set of measures that indicate the agreement of two independent label assignments strategies on the same dataset [33].
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