Backpropagation, or propagation of error, is a common method of teaching artificial neural networks how to perform a given task. It was first described by Arthur E.
Backpropagation is a kind of neural network. A Neural Network (or artificial neural network) is a collection of interconnected processing elements or nodes.
Although backpropagation can be applied to networks with any number of layers, ...
Backpropagation
The backpropagation algorithm is perhaps the most widely used training algorithm for multi-layered feedforward networks.
Error backpropagation. For hidden units, we must propagate the error back from the output nodes (hence the name of the algorithm). Again using the chain rule, we can expand the error of a hidden unit in terms of its posterior nodes: ...
Backpropagation An algorithm for efficiently calculating the error gradient of a neural network, which can then be used as the basis of learning.
error backpropagation learning algorithm The error backpropagation learning algorithm is a form of supervised learning used to train mainly feedforward neural networks to perform some task. In outline, the algorithm is as follows: ...
Backpropagation is a neural network learning algorithm. The field of neural networks was originally kindled by psychologists and neurobiologists who sought to develop and test computational analogues of neurons.
* "Backpropagation Neural Networks with One and Two Hidden Layers" by Jacques de Villiers and Etienne Barnard in IEEE Transactions on Neural Networks, vol 4, no 1, January 1992, pp 136-141. The bottom line here was: ...
[113] Paul Werbos discovered the backpropagation algorithm in 1984,[114] which led to a renaissance in neural network research and connectionism in general in the middle 1980s.
Multi-layer networks use a variety of learning techniques, the most popular being backpropagation. Here the output values are compared with the correct answer to compute the value of some predefined error-function.
Although the classic implementation of error driven learning (known as "backpropagation") has been criticized as biologically implausible from a cellular neuroscience perspective, ...
A very neat feature developed in this context is 'error-backpropagation'. This solves the problem of assigning the blame for bad prediction to individual neurons (aka the credit assignment problem).
"A neural network learning algorithm called Backpropagation is among the most effective approaches to machine learning when the data includes complex sensory input such as images.
Is each iteration of backpropagation guaranteed to bring the neural net closer to learning what it is supposed to learn, or could it just as easily cause it to regress?
There are many different ways of adjusting the weights, the most common for this type of problem is called backpropagation.
3 Backpropagation Learning 467
11.4 Competitive Learning 474
11.5 Hebbian Coincidence Learning 484
11.6 Attractor Networks or "Memories" 495
11.7 Epilogue and References 505
11.8 Exercises 506
12 MACHINE LEARNING: GENETIC AND EMERGENT 507
12.
 See also: Neural network, Artificial intelligence, Knowledge, AI, Genetic algorithm
  
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