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» Active Learning for Networked Data
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126
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NN
2000
Springer
167views Neural Networks» more  NN 2000»
15 years 3 months ago
Blind signal processing by the adaptive activation function neurons
The aim of this paper is to study an Information Theory based learning theory for neural units endowed with adaptive activation functions. The learning theory has the target to fo...
Simone Fiori
ICDM
2009
IEEE
199views Data Mining» more  ICDM 2009»
15 years 10 months ago
Active Learning with Adaptive Heterogeneous Ensembles
—One common approach to active learning is to iteratively train a single classifier by choosing data points based on its uncertainty, but it is nontrivial to design uncertainty ...
Zhenyu Lu, Xindong Wu, Josh Bongard
169
Voted
SDM
2008
SIAM
144views Data Mining» more  SDM 2008»
15 years 4 months ago
Active Learning with Model Selection in Linear Regression
Optimally designing the location of training input points (active learning) and choosing the best model (model selection) are two important components of supervised learning and h...
Masashi Sugiyama, Neil Rubens
134
Voted
ICML
2004
IEEE
15 years 9 months ago
Active learning using pre-clustering
The paper is concerned with two-class active learning. While the common approach for collecting data in active learning is to select samples close to the classification boundary,...
Hieu Tat Nguyen, Arnold W. M. Smeulders
122
Voted
PKDD
2010
Springer
164views Data Mining» more  PKDD 2010»
15 years 1 months ago
Complexity Bounds for Batch Active Learning in Classification
Active learning [1] is a branch of Machine Learning in which the learning algorithm, instead of being directly provided with pairs of problem instances and their solutions (their l...
Philippe Rolet, Olivier Teytaud