Fast connectionist learning: words and case

摘要

Some basic principles of connectionist research are explained along with an account of a number of the techniques necessary for constructing connectionist models. The objective is to introduce the area to people with limited mathematical and computational backgrounds by reducing the examples to simple arithmetic. In this way, a solid basis will be provided for one of the learning algorithms that have been fundamental to the development of network learning: the Hebbian learning rule. After outlining the technique in detail, two examples are provided to make the ideas concrete. These are learning to associate visual features with words and learning case representations.