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MCS
2009
Springer
9 years 6 months ago
When Semi-supervised Learning Meets Ensemble Learning
Abstract. Semi-supervised learning and ensemble learning are two important learning paradigms. The former attempts to achieve strong generalization by exploiting unlabeled data; th...
Zhi-Hua Zhou
MCS
2009
Springer
9 years 6 months ago
Constraints in Weighted Averaging
Weighted averaging of classifier outputs is used in many MCSs, yet is still not well understood. Several empirical studies have investigated the effect that non-negativity and su...
Amber Tomas
MCS
2009
Springer
9 years 6 months ago
A Study of Semi-supervised Generative Ensembles
Machine Learning can be divided into two schools of thought: generative model learning and discriminative model learning. While the MCS community has been focused mainly on the lat...
Manuela Zanda, Gavin Brown
MCS
2009
Springer
9 years 3 months ago
An Empirical Study of a Linear Regression Combiner on Multi-class Data Sets
The meta-learner MLR (Multi-response Linear Regression) has been proposed as a trainable combiner for fusing heterogeneous baselevel classifiers. Although it has interesting prope...
Chun-Xia Zhang, Robert P. W. Duin
MCS
2009
Springer
9 years 6 months ago
Selective Ensemble under Regularization Framework
An ensemble is generated by training multiple component learners for a same task and then combining them for predictions. It is known that when lots of trained learners are availab...
Nan Li, Zhi-Hua Zhou
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