There is a need of ensuring that learning (ML) models are interpretable. Higher
interpretability of the model means easier comprehension and explanation of future …

This chapter surveys and analyses visual methods of explainability of Machine Learning
(ML) approaches with focus on moving from quasi-explanations that dominate in ML to …

Established approaches to assuring safety-critical systems and software are difficult to apply
to systems employing ML where there is no clear, pre-defined specification against which to …

Chapter Overview In this chapter we provide a broad overview of selected knowledge
management, data mining, and text mining techniques and their use in various emerging …

We consider the predictions provided by the inductive theories. For these theories
predictions are performed by the Inductive Statistical (IS) inferences. It was noted by Hempel …

Abstract This chapter describes Data Mining in finance by discussing financial tasks,
specifics of methodologies and techniques in this Data Mining area. It includes time …

Objective Models of healthcare organizations (HCOs) are often defined up front by a select
few administrative officials and managers. However, given the size and complexity of …

The purpose of this work is to analise the cognitive process of the domain theories as it
described in the Measurement Theorry and to develop a computational Machine Learning …

Medical diagnose is a promise application that exploits data mining techniques. The
physicist diagnose, represented by human expertise, can be incurrence to fail. In contrast …

We developed an original approach to cognition, based on the previously developed theory
of neural modeling fields and dynamic logic. This approach is based on the detailed …