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On Combining Backpropagation with Boosting
The 2006 IEEE International Joint Conference on Neural Network Proceedings, 2006Boosting is a method for learning combined classifiers. In a boosting ensemble of classifiers trained by the backpropagation algorithm, the learning rate takes much smaller value comparing with the backpropagation applied alone. We propose a method which overcomes the above drawback and test it on neuro-fuzzy systems constituting a classifier ensemble ...
Marcin Korytkowski +2 more
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Backpropagation: past and future
IEEE International Conference on Neural Networks, 1988Some scientists have concluded that backpropagation is a specialized method for pattern classification, of little relevance to broader problems, to parallel computing, or to our understanding of the human brain. The author questions these beliefs and proposes development of a general theory of intelligence in which backpropagation and comparisons to ...
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A fuzzy backpropagation algorithm
Fuzzy Sets and Systems, 2000zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Stefka Stoeva, Alexander Nikov
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1990 IJCNN International Joint Conference on Neural Networks, 1990
Backpropagation learning can execute at supercomputer speed from training data sets of unprecedented size when supercomputer main memory is backed with newly available parallel arrays of commodity disk drives. An efficient implementation of backpropagation learning was modified and extended to iterate through training data sets stored on a parallel ...
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Backpropagation learning can execute at supercomputer speed from training data sets of unprecedented size when supercomputer main memory is backed with newly available parallel arrays of commodity disk drives. An efficient implementation of backpropagation learning was modified and extended to iterate through training data sets stored on a parallel ...
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Links between LVQ and Backpropagation
Pattern Recognition Letters, 1997Abstract In this paper we show that there are some intriguing links between the Backpropagation and LVQ algorithms. We show that Backpropagation used for training the weights of radial basis function networks exhibits an increasing competitive nature as the dispersion parameters decrease.
FRASCONI, PAOLO, M. GORI, SODA, GIOVANNI
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Modelling with constructive backpropagation
Neural Networks, 1999Neural network methods have proven to be powerful tools in modelling of nonlinear processes. One crucial part of modelling is the training phase where the model parameters are adjusted so that the model performs the desired operation as well as possible. Besides parameter estimation, an important problem is to select a suitable model structure.
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The Cost of Avoiding Backpropagation
CoRRForward-mode automatic differentiation (FmAD) and zero-order (ZO) optimization have been proposed as memory-efficient alternatives to backpropagation (BP) for gradient computation, especially in low-resource settings. However, their practical benefits remain unclear due to two key gaps: a lack of comparison against memory-efficient BP variants, such as
Kunjal Panchal +3 more
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Strictly local backpropagation
1990 IJCNN International Joint Conference on Neural Networks, 1990In conventional implementations of the backpropagation method, the delta error terms which propagate the error back through the network are global (common) variables shared by multiple nodes. A modified implementation of the algorithm which permits this process to occur using only local values of all variables is presented.
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From Backpropagation to Neurocontrol
2011This chapter provides an overview of the most powerful practical tools developed so far, and under development, in the areas which the Engineering Directorate of National Science Foundation has called “cognitive optimization and prediction”. It deals with a condensed overview of key tools and discusses the historical background and the larger ...
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