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ROCAI
2004
Springer
15 years 3 months ago
Learning Interestingness Measures in Terminology Extraction. A ROC-based approach
Abstract. In the field of Text Mining, a key phase in data preparation is concerned with the extraction of terms, i.e. collocation of words attached to specific concepts (e.g. Ph...
Mathieu Roche, Jérôme Azé, Yve...
ICASSP
2011
IEEE
14 years 1 months ago
Low-rank matrix completion by variational sparse Bayesian learning
There has been a significant interest in the recovery of low-rank matrices from an incomplete of measurements, due to both theoretical and practical developments demonstrating th...
S. Derin Babacan, Martin Luessi, Rafael Molina, Ag...
WSDM
2012
ACM
214views Data Mining» more  WSDM 2012»
13 years 5 months ago
Selecting actions for resource-bounded information extraction using reinforcement learning
Given a database with missing or uncertain content, our goal is to correct and fill the database by extracting specific information from a large corpus such as the Web, and to d...
Pallika H. Kanani, Andrew K. McCallum
PKDD
2009
Springer
118views Data Mining» more  PKDD 2009»
15 years 4 months ago
The Feature Importance Ranking Measure
Most accurate predictions are typically obtained by learning machines with complex feature spaces (as e.g. induced by kernels). Unfortunately, such decision rules are hardly access...
Alexander Zien, Nicole Krämer, Sören Son...
JMLR
2010
112views more  JMLR 2010»
14 years 5 months ago
Reduced-Rank Hidden Markov Models
Hsu et al. (2009) recently proposed an efficient, accurate spectral learning algorithm for Hidden Markov Models (HMMs). In this paper we relax their assumptions and prove a tighte...
Sajid M. Siddiqi, Byron Boots, Geoffrey J. Gordon