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1. |
Record Nr. |
UNINA9910461409703321 |
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Titolo |
Open access to STM information [[electronic resource] ] : trends, models and strategies for libraries / / edited by Anthi Katsirikou |
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Pubbl/distr/stampa |
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Berlin ; ; Boston, : De Gruyter Saur, c2011 |
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ISBN |
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1-283-40283-1 |
9786613402837 |
3-11-026374-2 |
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Descrizione fisica |
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1 online resource (208 p.) |
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Collana |
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IFLA publications, , 0344-6891 ; ; 153 |
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Classificazione |
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Altri autori (Persone) |
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Disciplina |
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Soggetti |
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Libraries and electronic publishing |
Open access publishing |
Science publishing |
Science and technology libraries - Collection development |
Institutional repositories |
Electronic books. |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Papers originally presented at the IFLA Satellite Pre-Conference held in Chania, Crete, Greece, August 6-8, 2010 in conjunction with the 76th IFLA General Conference and Assembly. |
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Nota di bibliografia |
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Includes bibliographical references. |
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Nota di contenuto |
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How to build an institutional repository : practical guide from a special library / Katalin Miszori -- Open access and academic library public services : roles for reference and instruction / Laura Bowering Mullen -- Marketing strategies for increasing the visibility of scientific research in the view of open access principles / Manolis Koukourakis and Angela Repanovici -- Managing virtual environments in libraries : Second Life and information literacy / Natassa Tsoubrakakou and Panorea Gaitanou -- Academic authors, scientific information, and open access publishing / Mirjana Brkovic -- Towards a new technology for science online : open access portals and social networking as a source of scientific information / Ana Ivkovic -- Open access and Web 2.0 convergence : information foundation of the future / Zoran Zdravkovic -- An institutional repository project as an organizational change vision in IRTA / Xantal Romaguera and Carmen Reverté -- Enhancing |
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institutional repositories (IR) in Ghana / R.B. Lamptey and A. Corletey -- Surabaya memory : opportunities and challenges of open access e-heritage repositories / Liauw Toong Tjiek (Aditya Ugraha) -- Developing a repository : a library's journey / Claire Bundy -- Open access and academic libraries journal subscriptions / Ageliki Oikonomou -- Copyright and open access journals in Greece / Assimina Vlachaki and Christine Urquhart -- Open access collaborative disciplinary repositories : an alternative publishing model / Roxana Theodorou and Ourania Konsta -- ZS project : zoological science meets institutional repositories / Sho Sato ... [et al.] -- Technology trends, requirements and models for providing sustainable technological support for libraries in an evolving environment / P. Stathopoulos, N. Houssos, and G. Stavrou -- Mapping the intellectual structure of open access field through co-citations / Guleda Duzyol, Zehra Taskin, and Yasar Tonta -- Open access books collection's improvement according to cost, user's satisfaction and user's demands / Aristeidis Meletiou. |
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Sommario/riassunto |
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This book contains a plethora of different viewpoints and research results from all over the world, bringing them together to provide a global perspectiveon the various issues that comprise ""open access"". Topics include copyright, best practices and management, open access and society, repositories, journals, publications and publishing, services and technology, quality andevaluation. The book offers a holistic focus on open access and can serve as a useful learning tool for students and professionals. |
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2. |
Record Nr. |
UNINA9910137704803321 |
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Titolo |
Robot Vision / / edited by Ales Ude |
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Pubbl/distr/stampa |
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[Place of publication not identified] : , : IntechOpen, , 2010 |
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ISBN |
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Descrizione fisica |
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1 online resource (626 pages) |
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Disciplina |
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Soggetti |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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3. |
Record Nr. |
UNINA9910299961303321 |
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Autore |
Cleophas Ton J |
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Titolo |
Machine Learning in Medicine - Cookbook Two / / by Ton J. Cleophas, Aeilko H. Zwinderman |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2014 |
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ISBN |
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Edizione |
[1st ed. 2014.] |
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Descrizione fisica |
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1 online resource (137 pages) : illustrations, tables |
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Collana |
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SpringerBriefs in Statistics, , 2191-544X |
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Disciplina |
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Soggetti |
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Medicine |
Biometry |
Statistics |
Application software |
Medicine/Public Health, general |
Biostatistics |
Statistics for Life Sciences, Medicine, Health Sciences |
Computer Applications |
Biometrics |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Nota di contenuto |
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Preface. I Cluster models -- Nearest Neighbors for Classifying New Medicines -- Predicting High-Risk-Bin Memberships -- Predicting Outlier Memberships -- Linear Models -- Polynomial Regression for Outcome Categories -- Automatic Nonparametric Tests for Predictor Categories- Random Intercept Models for Both Outcome and Predictor -- Automatic Regression for Maximizing Linear Relationships -- Simulation Models for Varying Predictors -- Generalized Linear Mixed Models for Outcome Prediction from Mixed Data -- Two Stage Least Squares for Linear Models with Problematic -- Autoregressive Models for Longitudinal Data. II Rules Models -- Item Response Modeling for Analyzing Quality of Life with Better Precision -- Survival Studies with Varying Risks of Dying -- Fuzzy Logic for Improved Precision of Pharmacological Data Analysis -- Automatic Data Mining for the Best Treatment of a Disease -- Pareto Charts for Identifying the Main Factors of Multifactorial -- Radial Basis Neural Networks for Multidimensional Gaussian -- Automatic Modeling for Drug Efficacy Prediction -- Automatic Modeling for Clinical Event Prediction -- Automatic Newton Modeling in Clinical Pharmacology -- Index. |
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Sommario/riassunto |
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The amount of data medical databases doubles every 20 months, and physicians are at a loss to analyze them. Also, traditional data analysis has difficulty to identify outliers and patterns in big data and data with multiple exposure / outcome variables and analysis-rules for surveys and questionnaires, currently common methods of data collection, are, essentially, missing. Consequently, proper data-based health decisions will soon be impossible. Obviously, it is time that medical and health professionals mastered their reluctance to use machine learning methods and this was the main incentive for the authors to complete a series of three textbooks entitled “Machine Learning in Medicine Part One, Two and Three, Springer Heidelberg Germany, 2012-2013", describing in a nonmathematical way over sixty machine learning methodologies, as available in SPSS statistical software and other major software programs. Although well received, it came to our attention that physicians and students often lacked time to read the entire books, and requested a small book, without background information and theoretical discussions and highlighting technical details. For this reason we produced a 100 page cookbook, entitled "Machine Learning in Medicine - Cookbook One", with data examples available at extras.springer.com for self-assessment and with reference to the above textbooks for background information. Already at the completion of this cookbook we came to realize, that many essential methods were not covered. The current volume, entitled "Machine Learning in Medicine - Cookbook Two" is complementary to the first and also intended for providing a more balanced view of the field and thus, as a must-read not only for physicians and students, but also for any one involved in the process and progress of health and health care. Similarly to Machine Learning in Medicine - Cookbook One, the current work will describe stepwise analyses of over twenty machine learning methods, that are, likewise, based on the three major machine learning methodologies: Cluster methodologies (Chaps. 1-3) Linear methodologies (Chaps. 4-11) Rules methodologies (Chaps. 12-20) In extras.springer.com the data files of the examples are given, as well as XML (Extended Mark up Language), SPS (Syntax) and ZIP (compressed) files for outcome predictions in future patients. In addition to condensed versions of the methods, fully described in the |
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above three textbooks, an introduction is given to SPSS Modeler (SPSS' data mining workbench) in the Chaps. 15, 18, 19, while improved statistical methods like various automated analyses and Monte Carlo simulation models are in the Chaps. 1, 5, 7 and 8. We should emphasize that all of the methods described have been successfully applied in practice by the authors, both of them professors in applied statistics and machine learning at the European Community College of Pharmaceutical Medicine in Lyon France. We recommend the current work not only as a training companion to investigators and students, because of plenty of step by step analyses given, but also as a brief introductory text to jaded clinicians new to the methods. For the latter purpose, background and theoretical information have been replaced with the appropriate references to the above textbooks, while single sections addressing "general purposes", "main scientific questions" and "conclusions" are given in place. Finally, we will demonstrate that modern machine learning performs sometimes better than traditional statistics does. Machine learning may have little options for adjusting confounding and interaction, but you can add propensity scores and interaction variables to almost any machine learning method. |
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