Traditional machine learning models were capable of inferring and generating data but with the developing technology, were replaced by better alternatives.
Everyday we come across news and updates about new ideas developed in the area of machine learning and artificial intelligence. Even though most of these innovations seem to be evolving over a period of time, the older or more traditional methods seem to be becoming obsolete.
Flipping an ML book from the 80s, we see that not all the machine learning techniques lasted for a long time because of certain limitations or scalability concerns/issues. While there exist plenty of useful machine learning methods, only a handful of them are put into practice. In this article, we will look into the now-redundant machine learning ideas.
In the 1960s, Stanford University professor Bernard Widrow and his doctoral student, Ted Hoff developed Adaptive Linear Neuron or ADALINE. It is an artificial neural network with a single-layer that uses memistors. The network consists of a weight, a bias and a summation function.
ADALINE was very useful in the field of telecommunications and engineering but its impact was very limited in cognitive science due to problems like linear separability, and its vulnerability to gradient explosion when an inappropriate learning rate is chosen.