Conference item icon

Conference item

Bayesian Optimization with an Empirical Hardness Model for Approximate Nearest Neighbour Search

Abstract:

Nearest Neighbour Search in high-dimensional spaces is a common problem in Computer Vision. Although no algorithm better than linear search is known, approximate algorithms are commonly used to tackle this problem. The drawback of using such algorithms is that their performance depends highly on parameter tuning. While this process can be automated using standard empirical optimization techniques, tuning is still time-consuming. In this paper, we propose to use Empirical Hardness Models to re...

Expand abstract

Actions


Access Document


Files:

Authors


Julieta Martinez More by this author
James Little More by this author
Nando de Freitas More by this author
Publication date:
2014
URN:
uuid:455a6df9-a949-4c4c-ba66-d5476bc9e22b
Local pid:
cs:8582

Terms of use


Metrics



If you are the owner of this record, you can report an update to it here: Report update to this record

TO TOP