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Conditional probability tree estimation analysis and algorithms

Beygelzimer, Alina and Langford, John and Lifshits, Yuri and Sorkin, Gregory B. and Strehl, Alex (2009) Conditional probability tree estimation analysis and algorithms. In: Uncertainty in artificial intelligence, 18-21 June 2009, Montreal, QC, Canada.

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Abstract

We consider the problem of estimating the conditional probability of a label in time $O(\log n)$, where $n$ is the number of possible labels. We analyze a natural reduction of this problem to a set of binary regression problems organized in a tree structure, proving a regret bound that scales with the depth of the tree. Motivated by this analysis, we propose the first online algorithm which provably constructs a logarithmic depth tree on the set of labels to solve this problem. We test the algorithm empirically, showing that it works succesfully on a dataset with roughly $10^6$ labels.

Item Type: Conference or Workshop Item (Paper)
Official URL: http://uai.sis.pitt.edu/displayArticles.jsp?mmnu=1...
Additional Information: © 2009 the Authors
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Sets: Research centres and groups > Management Science Group
Departments > Management
Date Deposited: 13 May 2011 10:37
Last Modified: 06 Jun 2012 17:16
URI: http://eprints.lse.ac.uk/id/eprint/35637

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