Machine Learning in Java - Second Edition

Perceptron

A perceptron is a basic neural network building block and one of the earliest supervised algorithms. It is defined as a sum of features, which is multiplied by the corresponding weights and a bias. When the input signals is received, it multiplies with the assigned weights. These weights are defined for each incoming signal or input, and the weight gets adjusted continuously during the learning phase. The adjustment of weight depends on the error of the last result. After multiplying with the respective weights, all of the inputs are summed up with some offset value called bias. The value of the bias is also adjusted by the weights. So, it starts with random weights and bias, and with each iteration, the weights and bias are adjusted ...