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» Predicting labels for dyadic data
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COLT
2008
Springer
13 years 11 months ago
Does Unlabeled Data Provably Help? Worst-case Analysis of the Sample Complexity of Semi-Supervised Learning
We study the potential benefits to classification prediction that arise from having access to unlabeled samples. We compare learning in the semi-supervised model to the standard, ...
Shai Ben-David, Tyler Lu, Dávid Pál
IDA
2010
Springer
13 years 11 months ago
Oracle Coached Decision Trees and Lists
This paper introduces a novel method for obtaining increased predictive performance from transparent models in situations where production input vectors are available when building...
Ulf Johansson, Cecilia Sönströd, Tuve L&...
ICDM
2009
IEEE
142views Data Mining» more  ICDM 2009»
13 years 7 months ago
Building Classifiers with Independency Constraints
In this paper we study the problem of classifier learning where the input data contains unjustified dependencies between some data attributes and the class label. Such cases arise...
Toon Calders, Faisal Kamiran, Mykola Pechenizkiy
ICDM
2009
IEEE
233views Data Mining» more  ICDM 2009»
14 years 4 months ago
Semi-Supervised Sequence Labeling with Self-Learned Features
—Typical information extraction (IE) systems can be seen as tasks assigning labels to words in a natural language sequence. The performance is restricted by the availability of l...
Yanjun Qi, Pavel Kuksa, Ronan Collobert, Kunihiko ...
SDM
2009
SIAM
105views Data Mining» more  SDM 2009»
14 years 7 months ago
Exploiting Semantic Constraints for Estimating Supersenses with CRFs.
The annotation of words and phrases by ontology concepts is extremely helpful for semantic interpretation. However many ontologies, e.g. WordNet, are too fine-grained and even hu...
Gerhard Paaß, Frank Reichartz