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» Learning to Select Useful Landmarks
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CVPR
2010
IEEE
16 years 2 months ago
Online Multiple Instance Learning with No Regret
Multiple instance (MI) learning is a recent learning paradigm that is more flexible than standard supervised learning algorithms in the handling of label ambiguity. It has been u...
Li Mu, James Kwok, Lu Bao-liang
COLT
2007
Springer
16 years 16 days 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
BMCBI
2005
155views more  BMCBI 2005»
15 years 6 months ago
Mining protein function from text using term-based support vector machines
Background: Text mining has spurred huge interest in the domain of biology. The goal of the BioCreAtIvE exercise was to evaluate the performance of current text mining systems. We...
Simon B. Rice, Goran Nenadic, Benjamin J. Stapley
IJON
2007
104views more  IJON 2007»
15 years 6 months ago
A probabilistic model of eye movements in concept formation
It has been unclear whether optimal experimental design accounts of data selection may offer insight into evidence acquisition tasks in which the learner’s beliefs change greatl...
Jonathan D. Nelson, Garrison W. Cottrell
ATAL
2008
Springer
15 years 8 months ago
MB-AIM-FSI: a model based framework for exploiting gradient ascent multiagent learners in strategic interactions
Future agent applications will increasingly represent human users autonomously or semi-autonomously in strategic interactions with similar entities. Hence, there is a growing need...
Doran Chakraborty, Sandip Sen