Advances in Mechanisms Design [Book] : Proceedings of TMM 2012 / Jaroslav Beran and 3 others, Editors.

The International Conference on the Theory of Machines and Mechanisms is organized every four years, under the auspices of the International Federation for the Promotion of Mechanism and Machine Science (IFToMM) and the Czech Society for Mechanics. This eleventh edition of the conference took place at the Technical University of Liberec, Czech Republic, 4-6 September 2012. This volume offers an international selection of the most important new results and developments, in 73 papers, grouped in seven different parts, representing a well-balanced overview, and spanning the general theory of machines and mechanisms, through analysis and synthesis of planar and spatial mechanisms, dynamics of machines and mechanisms, linkages and cams, computational mechanics, rotor dynamics, biomechanics, mechatronics, vibration and noise in machines, optimization of mechanisms and machines, control and monitoring systems of machines, accuracy and reliability of machines and mechanisms, robots and manipulators to the mechanisms of textile machines.

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Algorithmic Learning in a Random World describes recent theoretical and experimental developments in building computable approximations to Kolmogorov's algorithmic notion of randomness. Based on these approximations, a new set of machine learning algorithms have been developed that can be used to make predictions and to estimate their confidence and credibility in high-dimensional spaces under the usual assumption that the data are independent and identically distributed (assumption of randomness). Another aim of this unique monograph is to outline some limits of predictions: The approach based on algorithmic theory of randomness allows for the proof of impossibility of prediction in certain situations. The book describes how several important machine learning problems, such as density estimation in high-dimensional spaces, cannot be solved if the only assumption is randomness.

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