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MCS
2009
Springer
13 years 11 months ago
2009
Springer
Abstract. Semi-supervised learning and ensemble learning are two important learning paradigms. The former attempts to achieve strong generalization by exploiting unlabeled data; th...
MCS
2009
Springer
13 years 11 months ago
2009
Springer
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...
MCS
2009
Springer
13 years 11 months ago
2009
Springer
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...
MCS
2009
Springer
13 years 11 months ago
2009
Springer
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...
MCS
2009
Springer
13 years 9 months ago
2009
Springer
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...