With the aim of getting understandable symbolic rules to explain a given phenomenon, we
split the task of learning these rules from sensory data in two phases: a multilayer perceptron …

We consider an integrated subsymbolic–symbolic procedure for extracting symbolically
explained classification rules from data. A multilayer perceptron maps features into …

Once he discovered fire, man pondered its properties and ultimately developed the turbojet
engine. In this paper we will consider an extension of this approach to the case that the class …

This paper introduces a system that extracts comprehensible symbolic rules from a multi-
layer perceptron. Once the network has been trained in the usual manner, the training set is …

The last few years have produced a remarkable expansion of research in machine learning.
The field has gained an unprecedented popularity, several new areas have developed, and …

Machine learning is an important form of intelligent data analysis. It is common in machine
learning to distinguish symbolic and sub-symbolic approaches. Symbolic approaches …

A distinct advantage of symbolic learning algorithms over artificial neural networks is that
typically the concept representations they form are more easily understood by hu-mans. One …

In the past decade there has been a big effort in implementing automatic systems that can
be used in most environments and are able to decrease human work and therefore, human …

Hybrid intelligent systems that combine knowledge-based and artificial neural network
systems typically have four phases, involving domain knowledge representation, mapping of …

The field of machine learning strives to develop methods and techniques to automate the
acquisition of new information, new skills, and new ways of organizing existing information …