Theory in Machining (PDT112/3)

In recent years, considerable progress has been made in the understanding of problems of learning and generalization. In this context, Intelligence basically means the ability to perform well on new data after learning a model on the basis of given data. Such problems arise in many different areas and are becoming increasingly important and crucial towards many applications such as in bioinformatics, multimedia, computer vision and signal processing, internet search and information retrieval, datamining and textmining, finance, fraud detection, measurement systems and process control, and several others. Currently, the development of new technologies enables to generate massive amounts of data containing a wealth of information that remains to become explored. Often the dimensionality of the input spaces in these novel applications is huge. In the analysis of microarray data, for example, where expression levels of thousands of genes need to be analyzed given only a limited number of experiments. Without performing dimensionality reduction, the classical statistical paradigms show fundamental shortcomings at this point. Facing these new challenges, there is a need for new mathematical foundations and models such that the data can become processed in a reliable way. These subjects are very interdisciplinary and relate to problems studied in neural networks, machine learning, mathematics and statistics.

Generations: The Time Machine in Theory and Practice challenges the fragmented and diverse use of the concept of generation commonly found in the social sciences. Its innovative and exciting view of the field of generations stretches the sociological imagination away from its orientation towards the present and reconstitutes a fresh and dynamic way of seeing. It will be of particular interest to social theorists, anthropologists and sociologists of social history, consumption, identity and culture.

Machine learning is currently one of the most rapidly growing areas of research in computer science. In compiling this volume we have brought together contributions from some of the most prestigious researchers in this field. This book covers the three main learning systems; symbolic learning, neural networks and genetic algorithms as well as providing a tutorial on learning casual influences. Each of the nine chapters is self-contained. Both theoreticians and application scientists/engineers in the broad area of artificial intelligence will find this volume valuable. It also provides a useful sourcebook for Postgraduate since it shows the direction of current research.

This book contains the papers of the European Conference on Mechanisms Science (EUCOMES 2012 Conference). The book presents the most recent research developments in the mechanism and machine science field and their applications. Topics addressed are theoretical kinematics, computational kinematics, mechanism design, experimental mechanics, mechanics of robots, dynamics of machinery, dynamics of multi-body systems, control issues of mechanical systems, mechanisms for biomechanics, novel designs, mechanical transmissions, linkages and manipulators, micro-mechanisms, teaching methods, history of mechanism science and industrial and non-industrial applications.This volume will also serve as an interesting reference for the European activity in the fields of Mechanism and Machine Science as well as a source of inspirations for future works and developments.

The goal of this book is to address the use of several important machine learning techniques into computer vision applications. An innovative combination of computer vision and machine learning techniques has the promise of advancing the field of computer vision, which contributes to better understanding of complex real-world applications. The effective usage of machine learning technology in real-world computer vision problems requires understanding the domain of application, abstraction of a learning problem from a given computer vision task, and the selection of appropriate representations for the learnable (input) and learned (internal) entities of the system.In this book, we address all these important aspects from a new perspective: that the key element in the current computer revolution is the use of machine learning to capture the variations in visual appearance, rather than having the designer of the model accomplish this. As a bonus, models learned from large datasets are likely to be more robust and more realistic than the brittle all-design models.

Methods for the artificial evolution of active components, such as programs and hardware, are rapidly developing branches of adaptive computation and adaptive engineering. Evolvable Machines reports innovative and significant progress in automatic and evolutionary methodology applied to machine design. This book presents theoretical as well as practical chapters concentrating on Evolvable Robots, Evolvable Hardware Synthesis, as well as Evolvable Design.