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ICML
2006
IEEE
14 years 11 months ago
Active sampling for detecting irrelevant features
The general approach for automatically driving data collection using information from previously acquired data is called active learning. Traditional active learning addresses the...
Sriharsha Veeramachaneni, Emanuele Olivetti, Paolo...
PERCOM
2005
ACM
14 years 3 months ago
Applying Active Space Principles to Active Classrooms
Recent developments in pervasive computing have enabled new features for collaboration and instrumentation in educational technology systems. An infrastructure for the integration...
Chad Peiper, David Warden, Ellick Chan, Roy H. Cam...
LREC
2008
140views Education» more  LREC 2008»
13 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...
PKDD
2010
Springer
143views Data Mining» more  PKDD 2010»
13 years 8 months ago
A Unified Approach to Active Dual Supervision for Labeling Features and Examples
Abstract. When faced with the task of building accurate classifiers, active learning is often a beneficial tool for minimizing the requisite costs of human annotation. Traditional ...
Josh Attenberg, Prem Melville, Foster J. Provost
ICMCS
2000
IEEE
136views Multimedia» more  ICMCS 2000»
14 years 2 months ago
Low-Level Motion Activity Features for Semantic Characterization of Video
Efficient methods of content characterization for the browsing, retrieval or filtering of vast amount of digital video content has become a necessity. Still, there is a gap betwee...
Kadir A. Peker, A. Aydin Alatan, Ali N. Akansu