PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Noisy Search with Comparative Feedback
Shiau Hong Lim and Peter Auer
In: Event Title UNSPECIFIED(2011).


We present theoretical results in terms of lower and upper bounds on the query complexity of noisy search with comparative feedback. In this search model, the noise in the feedback depends on the distance between query points and the search target. Consequently, the error probability in the feedback is not fixed but varies for the queries posed by the search algorithm. Our results show that a target out of n items can be found in O(log n) queries. We also show the surprising result that for k possible answers per query, the speedup is not log k (as for k-ary search) but only loglog k in some cases.

PDF - Requires Adobe Acrobat Reader or other PDF viewer.
EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
Information Retrieval & Textual Information Access
ID Code:8474
Deposited By:Shiau Hong Lim
Deposited On:02 February 2012