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Single-layer neural networks can be trained using various learning algorithms. The best-known algorithms are the Adaline, Perceptron and Backpropagation algorithms for supervised learning. The first two are specific to single-layer neural networks while the third can be generalized to multi-layer perceptrons.-Single-layer neural networks can be train ed using various learning algorithms. The best - known algorithms are the Adaline. 102206 and Backpropagation algorithms fo r supervised learning. The first two are specific ic to single-layer neural networks while the th ird can be generalized to multi-layer perceptr ons.
Date : 2008-10-13 Size : 799.22kb User : 陈伟

Single-layer neural networks can be trained using various learning algorithms. The best-known algorithms are the Adaline, Perceptron and Backpropagation algorithms for supervised learning. The first two are specific to single-layer neural networks while the third can be generalized to multi-layer perceptrons.-Single-layer neural networks can be train ed using various learning algorithms. The best- known algorithms are the Adaline. 102206 and Backpropagation algorithms fo r supervised learning. The first two are specific ic to single-layer neural networks while the th ird can be generalized to multi-layer perceptr ons.
Date : 2025-07-04 Size : 799kb User : 陈伟

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A single perceptron code with backpropagation training algorithm designed for classification problems.
Date : 2025-07-04 Size : 1kb User : Paulo

DL : 0
The Adaline is essentially a single-layer backpropagation network. It is trained on a pattern recognition task, where the aim is to classify a bitmap representation of the digits 0-9 into the corresponding classes. Due to the limited capabilities of the Adaline, the network only recognizes the exact training patterns. When the application is ported into the multi-layer backpropagation network, a remarkable degree of fault-tolerance can be achieved.
Date : 2025-07-04 Size : 3kb User : ali
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