Conclusion Connectionist machine learning has proven to be a fruitful approach, and it
makes sense to investigate systems that combine the strengths of the symbolic and …

Data Mining: Concepts and Techniques, Fourth Edition introduces concepts, principles, and
methods for mining patterns, knowledge, and models from various kinds of data for diverse …

Our capabilities of both generating and collecting data have been increasing rapidly in the
last several decades. Contributing factors include the widespread use of bar codes for most …

The performance of symbolic inference tasks has long been a challenge to connectionists. In
this paper, we present an extended survey of this area. Existing connectionist inference …

A significant limitation of neural networks is that the represen (cid: 173) tations they learn are
usually incomprehensible to humans. We present a novel algorithm, TREPAN, for extracting …

It is becoming increasingly apparent that, without some form of explanation capability, the
full potential of trained artificial neural networks (ANNs) may not be realised. This survey …

Neural networks, despite their empirically proven abilities, have been little used for the
refinement of existing knowledge because this task requires a three-step process. First …

The present article is a novel attempt in providing an exhaustive survey of neuro-fuzzy rule
generation algorithms. Rule generation from artificial neural networks is gaining in …

This research project investigates the ability of neural networks, specifically, the
backpropagation algorithm, to integrate fundamental and technical analysis for financial …

The more intelligent systems based on sub-symbolic techniques pervade our everyday lives,
the less human can understand them. This is why symbolic approaches are getting more …