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COLT
2008
Springer
14 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
DAWAK
2010
Springer
14 years 9 months ago
Modelling Complex Data by Learning Which Variable to Construct
Abstract. This paper addresses a task of variable selection which consists in choosing a subset of variables that is sufficient to predict the target label well. Here instead of tr...
Françoise Fessant, Aurélie Le Cam, M...
ICPR
2002
IEEE
15 years 10 months ago
Relational Graph Labelling Using Learning Techniques and Markov Random Fields
This paper introduces an approach for handling complex labelling problems driven by local constraints. The purpose is illustrated by two applications: detection of the road networ...
Denis Rivière, Jean-Francois Mangin, Jean-M...
FLAIRS
2006
14 years 11 months ago
Corpus Based Unsupervised Labeling of Documents
Text categorization involves mapping of documents to a fixed set of labels. A similar but equally important problem is that of assigning labels to large corpora. With a deluge of ...
Delip Rao, Deepak P, Deepak Khemani
AI
2008
Springer
14 years 9 months ago
Label ranking by learning pairwise preferences
Preference learning is a challenging problem that involves the prediction of complex structures, such as weak or partial order relations, rather than single values. In the recent ...
Eyke Hüllermeier, Johannes Fürnkranz, We...