994 resultados para Machine-readable Library Cataloguing


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Quarterly update for Iowa Library Services/State Library patrons.

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Report on a special investigation of the Malvern Public Library for the period July 1, 2006 through October 31, 2013

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This guide is designed as a finding aid for researchers seeking materials on a particular topic and it indicates the nature and extent of resources for the study of United States history available at the Historical Society of Iowa, the Herbert Hoover Presidential Library and the Iowa University Libraries.

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Annual report for State Library of Iowa

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Annual report for State Library of Iowa

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Assesses the impact of library services on research projects, proposes methods to improve the impact of library services on research projects, assesses current library technology systems and proposes upgrades, assesses current library collection infrastructure and propose upgrades, especially in regards to collection damage from water or fire; ascertains patron interest in mobile technologies and suggest development based on interest.

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This leaflet gives information on how to start a free public library in a community. Includes regulations and related laws dealing with libraries.

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This leaflet includes information on programs for Women's Clubs. Some possible subjects for the club's discussions are literature, history and fine arts. Several methods of study are covered. Groups are encouraged to use public libraries and the Iowa Traveling Library to obtain reference materials for their topics.

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This leaflet gives information on the Iowa Library Commission and the added service of the circulation of books for the blind to the Traveling Library. Their goal is to make books available to every blind person who desires books.

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This leaflet talks about the architecture of library buildings. It provides information on the selection of an architect and how to design flooring and room arrangements in a practical way.

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Gives trustees a basic understanding of their responsibilities and power.

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Automatic environmental monitoring networks enforced by wireless communication technologies provide large and ever increasing volumes of data nowadays. The use of this information in natural hazard research is an important issue. Particularly useful for risk assessment and decision making are the spatial maps of hazard-related parameters produced from point observations and available auxiliary information. The purpose of this article is to present and explore the appropriate tools to process large amounts of available data and produce predictions at fine spatial scales. These are the algorithms of machine learning, which are aimed at non-parametric robust modelling of non-linear dependencies from empirical data. The computational efficiency of the data-driven methods allows producing the prediction maps in real time which makes them superior to physical models for the operational use in risk assessment and mitigation. Particularly, this situation encounters in spatial prediction of climatic variables (topo-climatic mapping). In complex topographies of the mountainous regions, the meteorological processes are highly influenced by the relief. The article shows how these relations, possibly regionalized and non-linear, can be modelled from data using the information from digital elevation models. The particular illustration of the developed methodology concerns the mapping of temperatures (including the situations of Föhn and temperature inversion) given the measurements taken from the Swiss meteorological monitoring network. The range of the methods used in the study includes data-driven feature selection, support vector algorithms and artificial neural networks.

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Quarterly update for Iowa Library Services/State Library patrons.

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This report includes the objectives, implementation responsibilities and target dates recommended and passed by the delegates to the Iowa Governor's Pre-White House Conference on Library and Information Services (April 30-May 2, 1991) or the White House Conference on Library and Information Services (July 9-13, 1991. Text of the relevant recommendations is included for each objective.

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The book presents the state of the art in machine learning algorithms (artificial neural networks of different architectures, support vector machines, etc.) as applied to the classification and mapping of spatially distributed environmental data. Basic geostatistical algorithms are presented as well. New trends in machine learning and their application to spatial data are given, and real case studies based on environmental and pollution data are carried out. The book provides a CD-ROM with the Machine Learning Office software, including sample sets of data, that will allow both students and researchers to put the concepts rapidly to practice.