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To Play the Game
13th October 1972: A Uruguayan Air Force plane, commissioned for a civilian flight, crashes in the Andes. Among the forty passengers are a first-division rugby team, accompanied by family and friends. Hindered by treacherous conditions, the search and rescue efforts cannot locate the wreckage, and are abandoned after eight days. Ten weeks later, two unkempt boys are spotted by a muleteer high in the Chilean foothills. One throws a note to him, across a mountain torrent: I come from a plane that fell in the mountains... In the plane there are still fourteen injured people... Drawing on extensive original research, the author sheds new light on this extraordinary story from a perspective of fi...
The Penny satirist and London pioneer [afterw.] The London pioneer [afterw.] The London literary pioneer [afterw.] Literary pioneer
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To Play the Game
A comprehensive history of the 1972 Andes Flight disaster. MONOCHROME EDITION, 571 pages and 275 images. Also available in COLOUR.
Trading on the Edge
Experts from the world's major financial institutions contributed to this work and have already used the newest technologies. Gives proven strategies for using neural networks, algorithms, fuzzy logic and nonlinear data analysis techniques to enhance profitability. The latest analytical breakthroughs, the impact on modern finance theory and practice, including the best ways for profitably applying them to any trading and portfolio management system, are all covered.
The Eleventh Annual Report of the Amalgamated Society of Carpenters and Joiners from December, 1869, to December1870
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Learning to Rank for Information Retrieval and Natural Language Processing, Second Edition
Learning to rank refers to machine learning techniques for training a model in a ranking task. Learning to rank is useful for many applications in information retrieval, natural language processing, and data mining. Intensive studies have been conducted on its problems recently, and significant progress has been made. This lecture gives an introduction to the area including the fundamental problems, major approaches, theories, applications, and future work. The author begins by showing that various ranking problems in information retrieval and natural language processing can be formalized as two basic ranking tasks, namely ranking creation (or simply ranking) and ranking aggregation. In rank...
Cost-Sensitive Machine Learning
In machine learning applications, practitioners must take into account the cost associated with the algorithm. These costs include: Cost of acquiring training dataCost of data annotation/labeling and cleaningComputational cost for model fitting, validation, and testingCost of collecting features/attributes for test dataCost of user feedback collect
Learning to Rank for Information Retrieval and Natural Language Processing
Learning to rank refers to machine learning techniques for training the model in a ranking task. Learning to rank is useful for many applications in information retrieval, natural language processing, and data mining. Intensive studies have been conducted on the problem recently and significant progress has been made. This lecture gives an introduction to the area including the fundamental problems, existing approaches, theories, applications, and future work. The author begins by showing that various ranking problems in information retrieval and natural language processing can be formalized as two basic ranking tasks, namely ranking creation (or simply ranking) and ranking aggregation. In r...
