Mining Technology Australia
Australia's Uranium Trade explores why the export of uranium remains a highly controversial issue in Australia and how this affects Australia's engagement with the strategic, regime and market realms of international nuclear affairs. The book focuses on the key challenges facing Australian policy makers in a twenty-first century context where civilian nuclear energy consumption is expanding significantly while at the same time the international nuclear nonproliferation regime is subject to increasing, and unprecedented, pressures. By focusing on Australia as a prominent case study, the book is concerned with how a traditionally strong supporter of the international nuclear nonproliferation regime is attempting to recalibrate its interest in maximizing the economic and diplomatic benefits of increased uranium exports during a period of flux in the strategic, regime and market realms of nuclear affairs. Australia's Uranium Trade provides broader lessons for how Ã¢" indeed whether Ã¢" nuclear suppliers worldwide are adapting to the changing nuclear environment internationally.
Data mining is a very active research area with many successful real-world app- cations. It consists of a set of concepts and methods used to extract interesting or useful knowledge (or patterns) from real-world datasets, providing valuable support for decision making in industry, business, government, and science. Although there are already many types of data mining algorithms available in the literature, it is still dif cult for users to choose the best possible data mining algorithm for their particular data mining problem. In addition, data mining al- rithms have been manually designed; therefore they incorporate human biases and preferences. This book proposes a new approach to the design of data mining algorithms. - stead of relying on the slow and ad hoc process of manual algorithm design, this book proposes systematically automating the design of data mining algorithms with an evolutionary computation approach. More precisely, we propose a genetic p- gramming system (a type of evolutionary computation method that evolves c- puter programs) to automate the design of rule induction algorithms, a type of cl- si cation method that discovers a set of classi cation rules from data. We focus on genetic programming in this book because it is the paradigmatic type of machine learning method for automating the generation of programs and because it has the advantage of performing a global search in the space of candidate solutions (data mining algorithms in our case), but in principle other types of search methods for this task could be investigated in the future.
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Mining Technology Australia