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This proceedings consists of fifty one selected papers presented at the 2015 International Workshop on Materials, Manufacturing Technology, Electronics and Information Science (MMTEI2015), which was successfully held in Wuhan, China during October 9-11, 2015.MMTEI2015 covered a wide range of fundamental studies, technical innovations and industrial applications in the 4 areas, namely Material Science and Application, Mechanical Engineering and Mechatronics,Electronics Engineering and Microelectronics, and Information Science.This workshop aims to provide a forum for scientists, scholars, engineers and students from universities all around the world and the industry to present ongoing research activities, and hence to foster research relations between universities and the industry. All accepted papers were subjected to a strict peer-review process by 2-3 expert referees.
This is the first book that integrates useful parametric and nonparametric techniques with time series modeling and prediction, the two important goals of time series analysis. Such a book will benefit researchers and practitioners in various fields such as econometricians, meteorologists, biologists, among others who wish to learn useful time series methods within a short period of time. The book also intends to serve as a reference or text book for graduate students in statistics and econometrics.
A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, practical, probabilistic approach to learning in kernel machines. Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning community over the past decade, and this book provides a long-needed systematic and unified treatment of theoretical and practical aspects of GPs in machine learning. The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics. The book deals with the supervised-learning problem for both regression and classification, and includes detailed algorithms. A wide variety of covariance (kernel) functions are presented and their properties discussed. Model selection is discussed both from a Bayesian and a classical perspective. Many connections to other well-known techniques from machine learning and statistics are discussed, including support-vector machines, neural networks, splines, regularization networks, relevance vector machines and others. Theoretical issues including learning curves and the PAC-Bayesian framework are treated, and several approximation methods for learning with large datasets are discussed. The book contains illustrative examples and exercises, and code and datasets are available on the Web. Appendixes provide mathematical background and a discussion of Gaussian Markov processes.
This book concerns the development of a theory of complex phenomena, using such concepts as fractals, chaos, and fractional derivatives; but, most important, the idea of an allometric control process is developed. In summary the theory attempts to explain why the distribution in the intensity of wars is the same as the relative frequency of the number of words used in languages and the number of species evolved over time from one or a few remote ancestors. The theory also describes the similarity in the variability of the number of births to teens in Texas to the number of sexual partners in homosexual liaisons. The data in both of the aforementioned categories are shown to have long-term memory, and it is this memory that also gives rise to inverse power laws in such physiological phenomena as the interbeat interval distribution of the human heart, the interstride interval distribution in the human gait, and memory in DNA sequences.
Computer application techniques are applied to routine short-term forecasting and prediction in this classic of operations research. The text begins with a consideration of data sources and sampling intervals, progressing to discussions of time series models and probability models. An extensive overview of smoothing techniques surveys the mathematical techniques for periodically raising the estimates of coefficients in forecasting problems. Sections on forecasting and error measurement and analysis are followed by an exploration of alternatives and the applications of the forecast to specific problems, and a treatment of the handling of systems design problems ranges from observed data to decision rules. 1963 ed.
Originally presented as the author's thesis (doctoral--Aarhas University).
Recent years have seen much new research on the interface between artificial intelligence and law, looking at issues such as automated legal reasoning. This collection of papers represents the state of the art in this fascinating and highly topical field.