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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...

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Publication date:
2014-01-01
URN:
uuid:455a6df9-a949-4c4c-ba66-d5476bc9e22b
Local pid:
cs:8582

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