A Topologically Distributed Encoding to Facilitate Learning

We describe how to solve linear-non-separable problems using simple feed-forward perceptrons without hidden layers. A biologically motivated topologically distributed encoding for input data is used. We point out advantages of neural networks compared to classic mathematical algorithms without loosing performance. The Iris-dataset from Fisher [1] is analyzed as a practical example. Keywords: Classification, Iris dataset, perceptrons, topologically distributed encoding Topologically Distributed Encoding 3 1 Introduction In this paper we examine the performance of neural classification networks dealing with real world problems. We show that neural networks can provide results comparable to mathematical methods (c.f. [2]). But in contrast to mathematical methods neural networks do not require applicability preconditions, which in most cases are not fulfilled. To clarify this we will give two examples of well known analytic methods. In our opinion the Bayesian decision theory has two s..