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138
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COLT
2007
Springer
15 years 10 months ago
Property Testing: A Learning Theory Perspective
Property testing deals with tasks where the goal is to distinguish between the case that an object (e.g., function or graph) has a prespecified property (e.g., the function is li...
Dana Ron
ICML
2009
IEEE
16 years 5 months ago
Proximal regularization for online and batch learning
Many learning algorithms rely on the curvature (in particular, strong convexity) of regularized objective functions to provide good theoretical performance guarantees. In practice...
Chuong B. Do, Quoc V. Le, Chuan-Sheng Foo
130
Voted
ICML
1996
IEEE
16 years 5 months ago
Learning Relational Concepts with Decision Trees
In this paper, we describe two di erent learning tasks for relational structures. When learning a classi er for structures, the relational structures in the training sets are clas...
Peter Geibel, Fritz Wysotzki
175
Voted
CVPR
2010
IEEE
15 years 2 months ago
P-N learning: Bootstrapping binary classifiers by structural constraints
This paper shows that the performance of a binary classifier can be significantly improved by the processing of structured unlabeled data, i.e. data are structured if knowing the ...
Zdenek Kalal, Jiri Matas, Krystian Mikolajczyk
148
Voted
CLEF
2006
Springer
15 years 8 months ago
Experimenting a "General Purpose" Textual Entailment Learner in AVE
In this paper we present the use of a "general purpose" textual entaiment recognizer in the Answer Validation Exercise (AVE) task. Our system has been developed to learn...
Fabio Massimo Zanzotto, Alessandro Moschitti