113
edits
Seminar on Internet Technologies (Winter 2014/2015): Difference between revisions
Jump to navigation
Jump to search
Seminar on Internet Technologies (Winter 2014/2015) (view source)
Revision as of 14:19, 27 October 2014
, 27 October 2014→Topics
(→Topics) |
(→Topics) |
||
| Line 65: | Line 65: | ||
| [http://dl.acm.org/citation.cfm?id=2594372][http://dl.acm.org/citation.cfm?id=2594387] | | [http://dl.acm.org/citation.cfm?id=2594372][http://dl.acm.org/citation.cfm?id=2594387] | ||
|- | |- | ||
|'''Efficient crowdsourcing for multi-class labeling''' | |'''Efficient crowdsourcing for multi-class labeling''' ''(Assigned to Berker Vardarsuyu)'' | ||
Crowdsourcing systems like Amazon's Mechanical Turk have emerged as an effective large-scale human-powered platform for performing tasks in domains such as image classification, data entry, recommendation, and proofreading. Since workers are low-paid (a few cents per task) and tasks performed are monotonous, the answers obtained are noisy and hence unreliable. To obtain reliable estimates, it is essential to utilize appropriate inference algorithms (e.g. Majority voting) coupled with structured redundancy through task assignment. | Crowdsourcing systems like Amazon's Mechanical Turk have emerged as an effective large-scale human-powered platform for performing tasks in domains such as image classification, data entry, recommendation, and proofreading. Since workers are low-paid (a few cents per task) and tasks performed are monotonous, the answers obtained are noisy and hence unreliable. To obtain reliable estimates, it is essential to utilize appropriate inference algorithms (e.g. Majority voting) coupled with structured redundancy through task assignment. | ||