Journal ArticleDOI
Ensemble learning via negative correlation
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The experimental results show that negative correlation learning can produce neural network ensembles with good generalisation ability.About:
This article is published in Neural Networks.The article was published on 1999-12-01. It has received 708 citations till now. The article focuses on the topics: Ensemble learning & Competitive learning.read more
Citations
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Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Accuracy
TL;DR: Although there are proven connections between diversity and accuracy in some special cases, the results raise some doubts about the usefulness of diversity measures in building classifier ensembles in real-life pattern recognition problems.
Book
How to Solve It: Modern Heuristics
TL;DR: In this article, the authors present a set of heuristics for solving problems with probability and statistics, including the Traveling Salesman Problem and the Problem of Who Owns the Zebra.
Journal ArticleDOI
Diversity creation methods: a survey and categorisation
TL;DR: This paper reviews the varied attempts to provide a formal explanation of error diversity, including several heuristic and qualitative explanations in the literature, and introduces the idea of implicit and explicit diversity creation methods, and three dimensions along which these may be applied.
Journal ArticleDOI
Multiobjective Deep Belief Networks Ensemble for Remaining Useful Life Estimation in Prognostics
TL;DR: A multiobjective deep belief networks ensemble (MODBNE) method that employs a multiobjectives evolutionary algorithm integrated with the traditional DBN training technique to evolve multiple DBNs simultaneously subject to accuracy and diversity as two conflicting objectives is proposed.
References
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Book ChapterDOI
Learning internal representations by error propagation
TL;DR: This chapter contains sections titled: The Problem, The Generalized Delta Rule, Simulation Results, Some Further Generalizations, Conclusion.
Book
Learning internal representations by error propagation
TL;DR: In this paper, the problem of the generalized delta rule is discussed and the Generalized Delta Rule is applied to the simulation results of simulation results in terms of the generalized delta rule.
Book
Machine Learning: Neural and Statistical Classification
TL;DR: A survey of previous comparisons and theoretical work descriptions of methods dataset descriptions criteria for comparison and methodology (including validation) empirical results machine learning on machine learning can be found in this article, where the authors also discuss their own work.
Journal ArticleDOI
Predicting chaotic time series
TL;DR: An error estimate is presented for this forecasting technique for chaotic data, and its effectiveness is demonstrated by applying it to several examples, including data from the Mackey-Glass delay differential equation, Rayleigh-Benard convection, and Taylor-Couette flow.
Journal ArticleDOI
A new evolutionary system for evolving artificial neural networks
TL;DR: The experimental results show that EPNet can produce very compact ANNs with good generalization ability in comparison with other algorithms, and has been tested on a number of benchmark problems in machine learning and ANNs.