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ECIR
2009
Springer
13 years 7 months ago
Active Learning Strategies for Multi-Label Text Classification
Abstract. Active learning refers to the task of devising a ranking function that, given a classifier trained from relatively few training examples, ranks a set of additional unlabe...
Andrea Esuli, Fabrizio Sebastiani
SIGIR
2011
ACM
12 years 12 months ago
Learning to rank from a noisy crowd
We study how to best use crowdsourced relevance judgments learning to rank [1, 7]. We integrate two lines of prior work: unreliable crowd-based binary annotation for binary classi...
Abhimanu Kumar, Matthew Lease
NIPS
2007
13 years 10 months ago
Reinforcement Learning in Continuous Action Spaces through Sequential Monte Carlo Methods
Learning in real-world domains often requires to deal with continuous state and action spaces. Although many solutions have been proposed to apply Reinforcement Learning algorithm...
Alessandro Lazaric, Marcello Restelli, Andrea Bona...
ICALT
2003
IEEE
14 years 2 months ago
Completing LOM - How Additional Axioms Increase the Utility of Learning Object Metadata
Learning Objects Metadata describing educational resources in order to allow better reusability and retrieval. Unfortunately, annotating complete courses thoroughly with LOM metad...
Jan Brase, Mark Painter, Wolfgang Nejdl
EUROCOLT
1999
Springer
14 years 1 months ago
Query by Committee, Linear Separation and Random Walks
Abstract. Recent works have shown the advantage of using Active Learning methods, such as the Query by Committee (QBC) algorithm, to various learning problems. This class of Algori...
Ran Bachrach, Shai Fine, Eli Shamir