YouTube: Graph Structure Learning from Unlabeled Data for Early Outbreak Detection

YouTube YouTube allows users to upload, view, rate, share, add to favorites, report, comment on videos, and subscribe to other users.

Graph Structure Learning from Unlabeled Data for Early Outbreak Detection May 23rd 2018, 15:32, by YouTube Processes such as disease propagation and information diffusion often spread over some latent network structure that must be learned from observation. Given a set of unlabeled training examples representing occurrences of an event type of interest (such as a disease outbreak), the authors aim to learn a graph structure that can be used to accurately detect future events of that type. They propose a novel framework for learning graph structure from unlabeled data by comparing the most anomalous subsets detected with and without the graph constraints. Their framework uses the mean normalized log-likelihood ratio score to measure the quality of a graph structure, and it efficiently searches for the highest-scoring graph structure. Using simulated disease outbreaks injected into real-world Emergency Department data from Allegheny County, the authors show that their method learns a structure similar to the true underlying graph, but enables faster and more accurate detection. Source: https://www.computer.org/csdl/mags/ex/2017/02/mex2017020080-abs.html     © Copyright 1994, All Rights Reserved,       https://www.facebook.com/357948684547280/videos/vb.357948684547280/615121118830034/?type=3&theater

You are receiving this email because you subscribed to this feed at blogtrottr.com. By using Blogtrottr, you agree to our policies, terms and conditions. If you no longer wish to receive these emails, you can unsubscribe from this feed, or manage all your subscriptions.