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JMLR
2012
13 years 5 months ago
Deterministic Annealing for Semi-Supervised Structured Output Learning
In this paper we propose a new approach for semi-supervised structured output learning. Our approach uses relaxed labeling on unlabeled data to deal with the combinatorial nature ...
Paramveer S. Dhillon, S. Sathiya Keerthi, Kedar Be...
ICALP
2001
Springer
15 years 7 months ago
Separating Quantum and Classical Learning
We consider a model of learning Boolean functions from quantum membership queries. This model was studied in [26], where it was shown that any class of Boolean functions which is i...
Rocco A. Servedio
132
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NLP
2000
15 years 6 months ago
Learning Rules for Large-Vocabulary Word Sense Disambiguation: A Comparison of Various Classifiers
In this article we compare the performance of various machine learning algorithms on the task of constructing word-sense disambiguation rules from data. The distinguishing characte...
Georgios Paliouras, Vangelis Karkaletsis, Ion Andr...
145
Voted
EACL
2006
ACL Anthology
15 years 4 months ago
Using Reinforcement Learning to Build a Better Model of Dialogue State
Given the growing complexity of tasks that spoken dialogue systems are trying to handle, Reinforcement Learning (RL) has been increasingly used as a way of automatically learning ...
Joel R. Tetreault, Diane J. Litman
ECCC
2008
117views more  ECCC 2008»
15 years 2 months ago
The complexity of learning SUBSEQ(A)
Higman essentially showed that if A is any language then SUBSEQ(A) is regular, where SUBSEQ(A) is the language of all subsequences of strings in A. Let s1, s2, s3, . . . be the sta...
Stephen A. Fenner, William I. Gasarch, Brian Posto...