NDLI: Negative correlation in incremental learning

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Negative correlation in incremental learning

Content Provider	Springer Nature Link
Author	Minku, Fernanda Li Iue, Hirotaka Yao, Xin
Copyright Year	2007
Abstract	Negative Correlation Learning (NCL) has been successfully applied to construct neural network ensembles. It encourages the neural networks that compose the ensemble to be different from each other and, at the same time, accurate. The difference among the neural networks that compose an ensemble is a desirable feature to perform incremental learning, for some of the neural networks can be able to adapt faster and better to new data than the others. So, NCL is a potentially powerful approach to incremental learning. With this in mind, this paper presents an analysis of NCL, aiming at determining its weak and strong points to incremental learning. The analysis shows that it is possible to use NCL to overcome catastrophic forgetting, an important problem related to incremental learning. However, when catastrophic forgetting is very low, no advantage of using more than one neural network of the ensemble to learn new data is taken and the test error is high. When all the neural networks are used to learn new data, some of them can indeed adapt better than the others, but a higher catastrophic forgetting is obtained. In this way, it is important to find a trade-off between overcoming catastrophic forgetting and using an entire ensemble to learn new data. The NCL results are comparable with other approaches which were specifically designed to incremental learning. Thus, the study presented in this work reveals encouraging results with negative correlation in incremental learning, showing that NCL is a promising approach to incremental learning.
Starting Page	289
Ending Page	320
Page Count	32
File Format	PDF
ISSN	15677818
Journal	Natural Computing
Volume Number	8
Issue Number	2
e-ISSN	15729796
Language	English
Publisher	Springer Netherlands
Publisher Date	2007-11-09
Publisher Place	Dordrecht
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Neural network ensembles Incremental learning Negative correlation learning Multi-layer perceptrons Self-generating neural tree Self-organising neural grove Classification Complexity Artificial Intelligence (incl. Robotics) Processor Architectures Evolutionary Biology Theory of Computation
Content Type	Text
Resource Type	Article
Subject	Computer Science Applications

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