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Retrospective Conversion Now in Paperback: History,

ISBN: , SKU: , AUTHOR: Schottlaender, Brian, PUBLISHER: Routledge, Retrospective Conversion is an essential guide for library catalogers and technical services managers in the process of converting manual catalog records to machine readable form. It clearly illustrates the advantages and disadvantages of the three conversion methods--converting in-house, contracting to a vendor, and a combination of the two--and covers the areas of cost, staff, time, and record quality for each. Catalogers will learn how to make a bigger investment in advance planning to achieve better end results. Helpful chapters emphasize the need for planning, quality control, and authority control in the creation of a complete catalog in a machine readable form. Also included are case studies that illustrate specific methods in action and provide a wealth of general, usable information.This unique reference covers a variety of valuable topics for catalogers involved in converting manual catalog records. It includes an in-depth bibliography and review of the retrospective conversion literature, including over 200 items addressing general considerations, special formats, and international issues. Specific examples of retrospective conversion are analyzed including projects in medium and large size institutions, non-serials and serials cataloging, music scores, and the Library of Congress's conversion of the PREMARC file. Specific techniques are explained such as the development of a PC workstation interface to facilitate the conversion process, the use of sampling techniques in project cost analysis, how to determine what quality standards are needed and at what cost, and authority control in both manual and online catalogs.

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Machine Learning and Statistics: The Interface

ISBN: , SKU: , AUTHOR: Nakhaeizadeh, Gholamreza / Taylor, C. C. / Nakhaeizadeh, G., PUBLISHER: Wiley-Interscience, The interface between statistics and machine learning (ML) is an increasingly popular research subject, as scientists and statisticians discover useful areas where these disciplines overlap. ML deals primarily with learning rules or structure, and while some books on the subject exist, this volume is the only one to integrate ML with statistics. It explores new areas where theory and methods can be shared and demonstrates the benefits to those working in either discipline. Written by leading experts in both fields, Machine Learning and Statistics is a result of the authors' participation in the European Conference in Machine Learning. This important collection of contributions was adapted from conference workshop material and reworked to address readers of diverse backgrounds and skills. For newcomers to the field, a thorough introduction surveys the various topics and supplies numerous references for further reading. The book's main focus is on classification, the most common area of intersection. The classification process uses information about a new example to assign the example to one of a known number of classes. Such methods typically involve a rule learned from an initial set of data, which is where ML comes into play. Other topics covered include prediction, control, and an introduction to methods of knowledge discovery in databases--a skill that has become especially relevant with the explosion in large-scale databases. Timely, practical, and innovative, this book offers a number of new algorithms and draws on real-world examples including financial and medical applications. It also includes two chapters on loans/credit applications that help identify bad risks and good customers--useful for those working with credit scoring and bad debt analysis. Machine Learning and Statistics is an invaluable resource for researchers involved with artificial intelligence and ML in academia, government, or industry, as well as those working with pattern recognition in statistical departments; for students at the graduate level who seek to expand their horizons; and for anyone who would like to learn more about these cutting-edge methodologies. The first book to explore the theory, methods, and applications of the relationship between machine learning and statistics. Here is the first book to address the growing demand for applications that integrate machine learning and statistics. The only text to focus on the interface between the two disciplines, this volume explores a mutually beneficial relationship that is fast becoming recognized by scientists, engineers, and researchers in data analysis and intelligent systems worldwide. The book shows that machine learning shares several areas of common research with statistics, most notably classification, prediction, and control. It demonstrates that statistical and probability methodologies can be applied in developing different machine learning algorithms and that these algorithms can

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