Deep Learning: Practical Neural Networks with Java

Logistic regression

We've covered that Neural Networks can work as data classifiers by establishing decision boundaries onto data in the hyperspace. This boundary can be linear, in the case of perceptrons, or nonlinear, in the case of other neural architectures such as MLPs, Kohonen, or Adaline. The linear case is based on linear regression, on which the classification boundary is a literally a line, as shown in the previous figure. If the scatter chart of the data looks like that of the following figure, then a nonlinear classification boundary is needed:

Neural Networks are in fact a great nonlinear classifier, and this is achieved by the usage ...