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Grant Award View - GA281763-V1
A Novel Approach to Semi-Supervised Statistical Machine Learning
GA ID:
GA281763-V1
Agency:
Australian Research Council
Approval Date:
19-Jan-2023
Variation Publish Date:
29-Mar-2023
Variation Date:
28-Mar-2023
Category:
Humanities, Arts and Social Sciences (HASS) Research
Grant Term:
1-Mar-2023 to 28-Feb-2026
Value (AUD):
$410,000.00
(GST inclusive where applicable)
Varies:
GA281763
- A Novel Approach to Semi-Supervised Statistical Machine Learning
One-off/Ad hoc:
No
Aggregate Grant Award:
No
PBS Program Name:
ARC 22/23 Discovery
Grant Program:
Discovery Projects
Grant Activity:
A Novel Approach to Semi-Supervised Statistical Machine Learning
Purpose:
Recent successes in the construction of classifiers for making diagnoses and predictions are due in part to their using much data labelled with respect to their class of origin. But typically there are little labelled data but plentiful unlabelled data. The goal of semi-supervised learning (SSL) is to leverage large amounts of unlabelled data to improve the performance using only small labelled datasets and so SSL is of paramount importance to applications where it is expensive or impractical to obtain much labelled data. The project is to develop a novel SSL approach that adopts a missingness mechanism for the missing labels to build a classifier that not only improves accuracy but it can be greater than if the missing labels were known.
GO ID:
GO Title:
Discovery Projects for funding commencing in 2023
Internal Reference ID:
DP23 Round 1
Selection Process:
Targeted or Restricted Competitive
Confidentiality - Contract:
No
Confidentiality - Outputs:
No
Grant Recipient Details
Recipient Name:
The University of Queensland
Recipient ABN:
63 942 912 684
Grant Recipient Location
Suburb:
ST LUCIA
Town/City:
ST LUCIA
Postcode:
4067
State/Territory:
QLD
Country:
AUSTRALIA
Grant Delivery Location
State/Territory:
QLD
Postcode:
4067
Country:
AUSTRALIA