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ALDT
2011
Springer
262views Algorithms» more  ALDT 2011»
14 years 4 months ago
Learning Complex Concepts Using Crowdsourcing: A Bayesian Approach
Abstract. We develop a Bayesian approach to concept learning for crowdsourcing applications. A probabilistic belief over possible concept definitions is maintained and updated acc...
Paolo Viappiani, Sandra Zilles, Howard J. Hamilton...
122
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SMA
2005
ACM
125views Solid Modeling» more  SMA 2005»
15 years 10 months ago
One-dimensional selections for feature-based data exchange
In the parametric feature based design paradigm, most features possess arguments that are subsets of the boundary of the current model, subsets defined interactively by user sele...
Ari Rappoport, Steven N. Spitz, Michal Etzion
ICPR
2006
IEEE
16 years 6 months ago
A New Data Selection Principle for Semi-Supervised Incremental Learning
Current semi-supervised incremental learning approaches select unlabeled examples with predicted high confidence for model re-training. We show that for many applications this dat...
Alexander I. Rudnicky, Rong Zhang
CVPR
2007
IEEE
15 years 11 months ago
Target Tracking with Online Feature Selection in FLIR Imagery
We present a particle filter-based target tracking algorithm for FLIR imagery. A dual foreground and background model is proposed for target representation which supports robust ...
Vijay Venkataraman, Guoliang Fan, Xin Fan
COLCOM
2005
IEEE
15 years 10 months ago
Symbiotic multi-path routing with attractor selection
In this paper we discuss the effects of symbiosis when using an attractor selection model for multi-path routing in an overlay network. Attractor selection is a biologically inspi...
Kenji Leibnitz, Naoki Wakamiya, Masayuki Murata