ADALINE (Adaptive Linear Neuron) is an artificial neural network model proposed by Bernard Widrow and Ted Hoff in 1960. It is similar to the perceptron, but instead of a step activation function, it uses a linear activation function.
ADALINE is a supervised learning model used to perform binary classification and linear regression. The neural network consists of an input layer, an output layer and a feedback layer that adjusts the weights of the input layer according to the output obtained.
The objective of ADALINE is to minimise the mean square error (MSE) between the desired output and the actual output of the network. It does this by using the gradient descent algorithm to adjust the input layer weights.
ADALINE is a linear model, which means that it can only learn linear relationships between inputs and outputs. However, it can be used as a basic unit in more complex neural network models, such as multilayer neural networks.
Typically, Machine Learning is used to solve business problems in various sectors and areas where different algorithms are applied.
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