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MICAI
2004
Springer
15 years 3 months ago
An Optimization Algorithm Based on Active and Instance-Based Learning
We present an optimization algorithm that combines active learning and locally-weighted regression to find extreme points of noisy and complex functions. We apply our algorithm to...
Olac Fuentes, Thamar Solorio
LREC
2008
140views Education» more  LREC 2008»
14 years 11 months ago
Toward Active Learning in Data Selection: Automatic Discovery of Language Features During Elicitation
Data Selection has emerged as a common issue in language technologies. We define Data Selection as the choosing of a subset of training data that is most effective for a given tas...
Jonathan Clark, Robert E. Frederking, Lori S. Levi...
EMNLP
2004
14 years 11 months ago
Active Learning and the Total Cost of Annotation
Active learning (AL) promises to reduce the cost of annotating labeled datasets for trainable human language technologies. Contrary to expectations, when creating labeled training...
Jason Baldridge, Miles Osborne
NIPS
2001
14 years 11 months ago
Active Learning in the Drug Discovery Process
We investigate the following data mining problem from Computational Chemistry: From a large data set of compounds, find those that bind to a target molecule in as few iterations o...
Manfred K. Warmuth, Gunnar Rätsch, Michael Ma...
CORR
2010
Springer
146views Education» more  CORR 2010»
14 years 10 months ago
Adaptive Submodularity: A New Approach to Active Learning and Stochastic Optimization
Solving stochastic optimization problems under partial observability, where one needs to adaptively make decisions with uncertain outcomes, is a fundamental but notoriously diffic...
Daniel Golovin, Andreas Krause