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» Evaluating machine learning for information extraction
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AUSAI
2008
Springer
15 years 8 months ago
Learning to Find Relevant Biological Articles without Negative Training Examples
Classifiers are traditionally learned using sets of positive and negative training examples. However, often a classifier is required, but for training only an incomplete set of pos...
Keith Noto, Milton H. Saier Jr., Charles Elkan
TSMC
2008
136views more  TSMC 2008»
15 years 6 months ago
Learning Relational Descriptions of Differentially Expressed Gene Groups
Abstract-- This paper presents a method that uses gene ontologies, together with the paradigm of relational subgroup discovery, to find compactly described groups of genes differen...
Igor Trajkovski, Filip Zelezný, Nada Lavrac...
TKDE
2010
224views more  TKDE 2010»
15 years 4 months ago
Probabilistic Topic Models for Learning Terminological Ontologies
—Probabilistic topic models were originally developed and utilised for document modeling and topic extraction in Information Retrieval. In this paper we describe a new approach f...
Wang Wei, Payam M. Barnaghi, Andrzej Bargiela
ICML
2008
IEEE
16 years 7 months ago
Exploration scavenging
We examine the problem of evaluating a policy in the contextual bandit setting using only observations collected during the execution of another policy. We show that policy evalua...
John Langford, Alexander L. Strehl, Jennifer Wortm...
ICASSP
2009
IEEE
15 years 10 months ago
Singing voice detection in music tracks using direct voice vibrato detection
In this paper we investigate the problem of locating singing voice in music tracks. As opposed to most existing methods for this task, we rely on the extraction of the characteris...
Lise Regnier, Geoffroy Peeters