The last decade has witnessed a revolution in interdisciplinary research where the boundaries of different areas have overlapped or even disappeared. New fields of research emerge each day where two or more fields have integrated to form a new identity. Examples of these emerging areas include bioinformatics (synthesizing biology with...

This book exclusively surveys the active on-going research of the current maturity of fuzzy logic over the last four decades. Many world leaders of fuzzy logic have enthusiastically contributed their best research results into five theoretical, philosophical and fundamental sub areas and nine distinctive applications, including two PhD...

The area of Neural computing that we shall discuss in this book represents a combination of techniques of classical optimization, statistics, and information theory. Neural network was once widely called artificial neural networks, which represented how the emerging technology was related to artificial intelligence. It once was a topic that...

This book presents in-depth surveys and systematic discussions on models, algorithms and applications for link mining. Link mining is an important field of data mining. Traditional data mining focuses on "flat" data in which each data object is represented as a fixed-length attribute vector. However, many real-world data sets are...

The development of pattern recognition methods on the basis of so-called Markov models is tightly coupled to the technological progress in the field of automatic speech recognition. Today, however, Markov chain and hidden Markov models are also applied in many other fields where the task is the modeling and analysis of chronologically...

This book suits both graduate students and researchers with a focus on discovering knowledge from scientific data. The use of computational power for data analysis and knowledge discovery in scientific disciplines has found its roots with the revolution of high-performance computing systems. Computational science in physics,...

Clustering is an important unsupervised classification technique where a set of patterns, usually vectors in multidimensional space, are grouped into clusters based on some similarity or dissimilarity criteria. In crisp clustering, each pattern is assigned to exactly one cluster, whereas in fuzzy clustering, each pattern is given a...

Statistical learning — that is, learning from data — and, in particular, probabilistic model learning have become increasingly important in recent years. Advances in information technology have facilitated an explosion of available data. This explosion has been accompanied by theoretical advances, permitting new and...

The present volume is dedicated to aspects of algorithmic work in bioinformat ics and computational biology with an emphasis on string algorithms that play a central role in the analysis of biological sequences. The papers included are a selection of articles corresponding to talks given at one of two meetings spon sored by The Royal...