An Active Learning Framework Incorporating User Input For Mining Urban Data
published: Oct. 25, 2016, recorded: August 2016, views: 946
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Analyzing and detecting events from ubiquitous sensors across the city has been an important goal in recent years. Different techniques that are able to automatically detect events by monitoring urban sensor’s data have been efficiently applied in several smart cities to improve the citizens everyday life. However, the analysis of such voluminous data streams often interferes with several constraints that arise in smart cities scenarios. For example it is impossible to hire human oracles that will monitor each data stream continuously to provide knowledge to these models and to annotate past instances. Thus, the development of novel techniques is required in order to build efficient supervised learning models that will be able to cope with urban data deluge. Our approach makes the following contributions: (i) we formulate the problem of building supervised learning models efficiently by incorporating streaming input from urban data, and (ii) we present a novel framework that is able to cope with the restrictions that arise in the event detection of streaming urban data, requiring labels from carefully selected instances.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !