Book Chapter10.1007/978-3-540-92137-0_1
A Balanced Ensemble Learning with Adaptive Error Functions
Yong Liu
- 19 Dec 2008
- pp 1-8
29
TL;DR: A novel balanced ensemble learning approach that could make learning fast and robust in an ensemble, in which different individuals could have different formats of error functions in the learning process, and these error functions could be changed as well.
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Abstract: In the practice of designing neural network ensembles, it is common that a certain learning error function is defined and kept the same or fixed for each individual neural network in the whole learning process. Such fixed learning error function not only likely leads to over-fitting, but also makes learning slow on hard-learned data points in the data set. This paper presents a novel balanced ensemble learning approach that could make learning fast and robust. The idea of balanced ensemble learning is to define adaptive learning error functions for different individual neural networks in an ensemble, in which different individuals could have different formats of error functions in the learning process, and these error functions could be changed as well. Through shifting away from well-learned data and focusing on not-yet-learned data by changing error functions for each individual among the ensemble, a good balanced learning could be achieved for the learned ensemble.
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Citations
Balanced Learning for Ensembles with Small Neural Networks
Yong Liu
- 01 Oct 2009
TL;DR: It is discovered that learners by balanced ensemble learning could be just be slightly better than random guessing even if they had been trained on the whole data set.
24
Balancing ensemble learning through error shif
Yong Liu
- 01 Oct 2011
TL;DR: This paper shows how balanced ensemble learning could guide learning to being less biased through error shift, and create weak learners in an ensemble.
22
Create weak learners with small neural networks by balanced ensemble learning
Yong Liu
- 27 Oct 2011
TL;DR: The experimental results suggest that balanced ensemble learning is able to create learners being both weak and negatively correlated.
22
Target shift awareness in balanced ensemble learning
Yong Liu
- 01 Sep 2011
TL;DR: This paper is to explore how the target shift awareness could help to decide a decision boundary that is neither too close nor too further to all training samples.
21
Error Awareness by Lower and Upper Bounds in Ensemble Learning
TL;DR: Experimental results would explore how LBER and UBEO would lead negative correlation learning towards a better decision boundary.
13
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Neural network ensembles
Lars Kai Hansen,Peter Salamon +1 more
TL;DR: It is shown that the remaining residual generalization error can be reduced by invoking ensembles of similar networks, which helps improve the performance and training of neural networks for classification.
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Task Decomposition Through Competition in a Modular Connectionist Architecture: The What and Where Vision Tasks
TL;DR: A novel modular connectionist architecture is presented in which the networks composing the architecture compete to learn the training patterns, and an outcome of the competition is that different networks learn different training patterns and, thus, learn to compute different functions.
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