Open AccessBook Chapter
Active learning with mixture models
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The article was published on 1997-09-16 and is currently open access. It has received 10 citations till now. The article focuses on the topics: Active learning (machine learning).read more
Citations
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A probability analysis on the value of unlabeled data for classification problems
Tong Zhang,Frank J. Oles +1 more
TL;DR: It is demonstrated that Fisher information matrices can be used to judge the asymp-totic value of unlabeled data and this methodology is applied to both passive partially supervised learning and active learning.
Proceedings ArticleDOI
Active learning using adaptive resampling
TL;DR: An active learning method is presented that uses adaptive resampling in a natural way to signi cantly reduce the size of the required labeled set and generates a classi cation model that achieves the high accuracies possible with current adaptive Resampling methods.
Proceedings ArticleDOI
A new analysis of the value of unlabeled data in semi-supervised learning for image retrieval
TL;DR: A new analysis on the value of unlabeling data is provided by considering different distributions of the labeled and unlabeled data and showing the migrating effect for semi-supervised learning.
Dissertation
Implementation of gaussian process models for non-linear system identification
TL;DR: This thesis is concerned with investigating the use of Gaussian Process (GP) models for the identification of nonlinear dynamic systems from an engineering perspective and the implementation aspects of the GP model are the main focus.
Journal ArticleDOI
Efficient estimation of material property curves and surfaces via active learning
TL;DR: In this article, the authors compare a number of strategies based on directed exploration on several materials problems of varying complexity using a Kriging-based model and find that directed exploration guided by maximum variance provides better performance overall, converging faster across several data sets.