Using the perceptron learning algorithm to classify linearly separable data
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README.md
pla_classifier.py

README.md

perceptron_classification

Using the perceptron learning algorithm to classify linearly separable data

The perceptron learning algorithm (PLA) is a classificiation algorithm that can be successfully applied to any linearly separable data: meaning, that for a D-dimensional data set, a (D-1)-dimensional hyperplane exists that separates the classes [Mitchell, 86]. The PLA has the property that if such a hyperplane exists, the algorithm will ultimately converge on a solution [Lin, et al., 8]. In cases where the data is not linearly separable, methods exist for approximating a solution (the delta rule and gradient descent) or moving past linear separation altogether (the sigmoid unit) [Mitchell, 96].