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What is Perceptron Learning Rule Explained about?
The perceptron learning rule is an algorithm for training a single-layer perceptron to classify input patterns into two categories. It works by iteratively adjusting the weights of connections between input and output neurons based on whether patterns are correctly or incorrectly classified. Specifically, if a pattern is misclassified, the weight vector is updated by adding an amount proportional to the product of the input pattern and the desired output. This process is repeated over all training patterns
- Author
- Gabriel Kirk
- Language
- EN