Sentence examples for collective inference from inspiring English sources

Jensen, D., Neville, J. & Gallagher, B. Why collective inference improves relational classification.

The collective inference paradigm allows us to manage the spatial correlation between spectral responses of neighboring pixels, as interacting pixels are labeled simultaneously.

As a result of the study, the best configuration is a non-relational learner enriched with network variables, without collective inference, using binary weights and undirected networks.

We illustrate this empirically by experiments on approximate inference in Markov logic networks using LP relaxations, on solving Markov decision processes, and on collective inference using LP support vector machines.

We statistically evaluate the effect of relational classifiers and collective inference methods on the predictive power of relational learners, as well as the performance of models where relational learners are combined with traditional methods of predicting customer churn in the telecommunication industry.

This approach not only ensures that there is the possibility of selecting those rules or patterns that, taken individually, carry the greatest amount of information (for example, have low degrees and high coverages); it also maximizes the collective inference power of the selected family of patterns.

We proceed by formulating a collective network inference model, wherein a network is jointly estimated from multiple nonidentical data distributions.

The importance of latent factor parameterization will be obvious later in Section 3.7 when we discuss collective network inference from many datasets.

In addition, the data available from experimental studies on mammalian systems are not complete or sufficiently time-resolved making collective parameter inference a non-trivial task.

In collective network inference, we solve for: (13) min ⁡ U, Q x, Q y, W x, W y ∑ s ∈ V (ℓ s ; P x (U, Q x, W x ; D x ) + ℓ s ; P y (U, Q y, W y ; D y ) ) + reg.

This study also demonstrates that meta-analysis may be poorly suited for synthesizing results of observational studies with heterogeneous settings, methods, and exposure levels that require careful interpretation of individual and collective results for causal inference [ 59, 60].