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» MILIS: Multiple Instance Learning with Instance Selection
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PKDD
2010
Springer
162views Data Mining» more  PKDD 2010»
13 years 6 months ago
Expectation Propagation for Bayesian Multi-task Feature Selection
In this paper we propose a Bayesian model for multi-task feature selection. This model is based on a generalized spike and slab sparse prior distribution that enforces the selectio...
Daniel Hernández-Lobato, José Miguel...
ICML
2009
IEEE
14 years 8 months ago
Robust bounds for classification via selective sampling
We introduce a new algorithm for binary classification in the selective sampling protocol. Our algorithm uses Regularized Least Squares (RLS) as base classifier, and for this reas...
Nicolò Cesa-Bianchi, Claudio Gentile, Franc...
ECML
2007
Springer
14 years 1 months ago
An Improved Model Selection Heuristic for AUC
Abstract. The area under the ROC curve (AUC) has been widely used to measure ranking performance for binary classification tasks. AUC only employs the classifier’s scores to ra...
Shaomin Wu, Peter A. Flach, Cèsar Ferri Ram...
ICDM
2010
IEEE
128views Data Mining» more  ICDM 2010»
13 years 5 months ago
User-Based Active Learning
Active learning has been proven a reliable strategy to reduce manual efforts in training data labeling. Such strategies incorporate the user as oracle: the classifier selects the m...
Christin Seifert, Michael Granitzer
ICCV
2009
IEEE
15 years 20 days ago
Joint learning of visual attributes, object classes and visual saliency
We present a method to learn visual attributes (eg.“red”, “metal”, “spotted”) and object classes (eg. “car”, “dress”, “umbrella”) together. We assume imag...
Gang Wang, David Forsyth