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MLDM
2007
Springer
14 years 3 months ago
Transductive Learning from Relational Data
Transduction is an inference mechanism “from particular to particular”. Its application to classification tasks implies the use of both labeled (training) data and unlabeled (...
Michelangelo Ceci, Annalisa Appice, Nicola Barile,...
ROCAI
2004
Springer
14 years 2 months ago
Learning Mixtures of Localized Rules by Maximizing the Area Under the ROC Curve
We introduce a model class for statistical learning which is based on mixtures of propositional rules. In our mixture model, the weight of a rule is not uniform over the entire ins...
Tobias Sing, Niko Beerenwinkel, Thomas Lengauer
KDD
2009
ACM
224views Data Mining» more  KDD 2009»
14 years 1 months ago
Issues in evaluation of stream learning algorithms
Learning from data streams is a research area of increasing importance. Nowadays, several stream learning algorithms have been developed. Most of them learn decision models that c...
João Gama, Raquel Sebastião, Pedro P...
AUSDM
2006
Springer
144views Data Mining» more  AUSDM 2006»
14 years 19 days ago
A Characterization of Wordnet Features in Boolean Models For Text Classification
Supervised text classification is the task of automatically assigning a category label to a previously unlabeled text document. We start with a collection of pre-labeled examples ...
Trevor N. Mansuy, Robert J. Hilderman
SDM
2010
SIAM
144views Data Mining» more  SDM 2010»
13 years 10 months ago
A Probabilistic Framework to Learn from Multiple Annotators with Time-Varying Accuracy
This paper addresses the challenging problem of learning from multiple annotators whose labeling accuracy (reliability) differs and varies over time. We propose a framework based ...
Pinar Donmez, Jaime G. Carbonell, Jeff Schneider