Urszula Stańczyk – författare

Man-machine interaction is the interdisciplinary field, focused on a human and a machine in conjunction. It is the intersection of computer science, behavioural sciences, social psychology, ergonomics, security. It encompasses study, design, implementation, and evaluation of small- and large-scale, interacting, computing, hardware and software systems dedicated for human use. Man-machine interaction builds on supportive knowledge from both sides, the machine side providing techniques, methods and technologies relevant for computer graphics, visualisation, programming environments, the human side bringing elements of communication theory, linguistics, social sciences, models of behaviour. The discipline aims to improve ways in which machines and their users interact, making hardware and software systems better adapted to user''s needs, more usable, more receptive, and optimised for desired properties.

This monograph is the second edition in the series, providing the reader with a selection of high-quality papers dedicated to current progress, new developments and research trends in man-machine interactions area. In particular, the topical subdivisions of this volume include human-computer interfaces, robot control and navigation systems, bio-data analysis and mining, pattern recognition for medical applications, sound, text and image processing, design and decision support, rough and fuzzy systems, crisp and fuzzy clustering, prediction and regression, algorithms and optimisation, and data management systems.

This research book provides the reader with a selection of high-quality texts dedicated to current progress, new developments and research trends in feature selection for data and pattern recognition.

Even though it has been the subject of interest for some time, feature selection remains one of actively pursued avenues of investigations due to its importance and bearing upon other problems and tasks.

This volume points to a number of advances topically subdivided into four parts: estimation of importance of characteristic features, their relevance, dependencies, weighting and ranking; rough set approach to attribute reduction with focus on relative reducts; construction of rules and their evaluation; and data- and domain-oriented methodologies.