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COLT
1989
Springer
13 years 9 months ago
Learning in the Presence of Inaccurate Information
The present paper considers the effects of introducing inaccuracies in a learner’s environment in Gold’s learning model of identification in the limit. Three kinds of inaccu...
Mark A. Fulk, Sanjay Jain
ICMCS
2006
IEEE
125views Multimedia» more  ICMCS 2006»
13 years 11 months ago
Label Disambiguation and Sequence Modeling for Identifying Human Activities from Wearable Physiological Sensors
Wearable physiological sensors can provide a faithful record of a patient’s physiological states without constant attention of caregivers. A computer program that can infer huma...
Wei-Hao Lin, Alexander G. Hauptmann
ALT
2007
Springer
14 years 2 months ago
Learning Kernel Perceptrons on Noisy Data Using Random Projections
In this paper, we address the issue of learning nonlinearly separable concepts with a kernel classifier in the situation where the data at hand are altered by a uniform classific...
Guillaume Stempfel, Liva Ralaivola
ICDM
2010
IEEE
228views Data Mining» more  ICDM 2010»
13 years 2 months ago
Active Learning from Multiple Noisy Labelers with Varied Costs
In active learning, where a learning algorithm has to purchase the labels of its training examples, it is often assumed that there is only one labeler available to label examples, ...
Yaling Zheng, Stephen D. Scott, Kun Deng
AII
1992
13 years 9 months ago
Learning from Multiple Sources of Inaccurate Data
Most theoretical models of inductive inference make the idealized assumption that the data available to a learner is from a single and accurate source. The subject of inaccuracies ...
Ganesh Baliga, Sanjay Jain, Arun Sharma