The paper examines 29 distinct formulations of utility score and selection methods reporting their impact on the results of two collective classification ...
The paper examines 29 distinct formulations of utility score and selection methods reporting their impact on the results of two collective classification ...
The paper examines 29 distinct formulations of utility score and selection methods reporting their impact on the results of two collective classification ...
Learning in unlabeled networks – An active learning and inference approach. https://doi.org/10.3233/aic-150686 · Full text. Journal: AI Communications, 2015 ...
Learning in unlabeled networks - an active learning and inference approach ... learning, social network and media analysis, and machine learning.
Unlabeled networks, on the other hand, have been less studied, but are now emerging as relevant objects in many areas including differential privacy and ...
Learning in Unlabeled Networks - An Active Learning and Inference Approach · no code implementations • 5 Oct 2015 • Tomasz Kajdanowicz, Radosław Michalski ...
Apr 30, 2023 · Usually the active learning / uncertainty sampling approach would be to take a bunch of unlabeled data points, run model inference on them ...
The method for active learning and inference in within network classification based on node selection is proposed in the paper.
Missing: Unlabeled | Show results with:Unlabeled
Bibliographic details on Learning in Unlabeled Networks - An Active Learning and Inference Approach.