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[null,"1.","Record Nr.",null,"UNISALENTO991002142729707536"]
[null,null,"Autore",null,"Italia : Camera dei deputati : Biblioteca"]
[null,null,"Titolo",null,"Bibliografia dei periodici del periodo fascista 1922-1945 : posseduti dalla biblioteca della Camera dei deputati / [a cura di] Dora Gulli Pecenko, Laura Nasi Zitelli ; introduzione di Renzo De Felice"]
[null,null,"Pubbl/distr/stampa",null,"Roma : Camera dei Deputati, 1983, stampa 1984"]
[null,null,"Descrizione fisica",null,"269 p. ; 24 cm."]
[null,null,"Disciplina",null,"016.945091505"]
[{"$nulls":4},"015.45034"]
[null,null,"Soggetti",null,"Periodici fascisti - Bibliografia"]
[{"$nulls":4},"Italia Camera dei deputati Biblioteca Cataloghi"]
[null,null,"Lingua di pubblicazione",null,"Italiano"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,"2.","Record Nr.",null,"UNISA996203938103316"]
[null,null,"Titolo",null,"Ocular tumors in animals and humans [[electronic resource] /] / edited by Robert L. Peiffer, Jr. and Kenneth B. Simons"]
[null,null,"Pubbl/distr/stampa",null,"Ames, : Iowa State Press, 2002"]
[null,null,"ISBN",null,"1-281-81453-9"]
[{"$nulls":4},"9786611814533"]
[{"$nulls":4},"0-470-79255-8"]
[{"$nulls":4},"0-470-37690-2"]
[{"$nulls":4},"0-470-37667-8"]
[null,null,"Edizione",null,"[1st ed.]"]
[null,null,"Descrizione fisica",null,"1 online resource (484 p.)"]
[null,null,"Disciplina",null,"616.99"]
[{"$nulls":4},"616.99/284"]
[{"$nulls":4},"616.99284"]
[null,null,"Soggetti",null,"Eye - Tumors - Pathophysiology"]
[{"$nulls":4},"Veterinary ophthalmology"]
[{"$nulls":4},"Tumors in animals"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Description based upon print version of record."]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"OCULAR TUMORS IN ANIMALS AND HUMANS; CONTENTS; Contributors; Foreword; Preface; Acknowledgments; 1. Orbital Tumors; 2. Eyelid Tumors; 3. Tumors of the Conjunctiva; 4. Neoplasms of the Lacrimal Drainage System; 5. Tumors of the Cornea and Sclera; 6. Iridociliary Epithelial Tumors; 7. Uveal Melanocytic Tumors; 8. Feline Ocular Sarcomas..; 9. Optic Nerve Neoplasia; 10. Retinoblastoma in Humans and Animals; 11. Metastases; 12. Ocular Lymphoid Proliferations; 13. The Phakomatoses; 14. Experimental Ocular Oncology: Animal Models; Index"]
[null,null,null,"Sommario/riassunto","The application of evidence-based veterinary medicine (EBVM) can assist in improving and optimising the diagnosis, prognosis, control, treatment and ultimately the welfare of animals. It can also provide the user with a methodology for appropriate, patient orientated life-long, self-directed, learning. To practise evidence-based veterinary medicine we require a range of skills that we may not have. This book will explain what evidence-based veterinary medicine is and show how it can be applied to veterinary practice to improve the quality of care for "]
[{"$nulls":4},"patients and provide informed choices"]
[null,"3.","Record Nr.",null,"UNINA9910337856403321"]
[null,null,"Autore",null,"Mirkin Boris"]
[null,null,"Titolo",null,"Core Data Analysis: Summarization, Correlation, and Visualization / / by Boris Mirkin"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019"]
[null,null,"ISBN",null,"3-030-00271-3"]
[null,null,"Edizione",null,"[2nd ed. 2019.]"]
[null,null,"Descrizione fisica",null,"1 online resource (XV, 524 p. 187 illus., 80 illus. in color.)"]
[null,null,"Collana",null,"Undergraduate Topics in Computer Science, , 2197-1781"]
[null,null,"Disciplina",null,"519.50285"]
[{"$nulls":4},"519.535"]
[null,null,"Soggetti",null,"Artificial intelligence - Data processing"]
[{"$nulls":4},"Data protection"]
[{"$nulls":4},"Data mining"]
[{"$nulls":4},"Computer science - Mathematics"]
[{"$nulls":4},"Data Science"]
[{"$nulls":4},"Data and Information Security"]
[{"$nulls":4},"Data Mining and Knowledge Discovery"]
[{"$nulls":4},"Mathematical Applications in Computer Science"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"Topics in Data Analysis Substance -- Quantitative Summarization -- Learning Correlations -- Core Partitioning: K-Means and Similarity Clustering -- Divisive and Separate Cluster Structures -- Appendix. Basic Math and Code -- Index."]
[null,null,null,"Sommario/riassunto","This text examines the goals of data analysis with respect to enhancing knowledge, and identifies data summarization and correlation analysis as the core issues. Data summarization, both quantitative and categorical, is treated within the encoder-decoder paradigm bringing forward a number of mathematically supported insights into the methods and relations between them. Two Chapters describe methods for categorical summarization: partitioning, divisive clustering and separate cluster finding and another explain the methods for quantitative summarization, Principal Component Analysis and PageRank. Features: · An in-depth presentation of K-means partitioning including a corresponding Pythagorean decomposition of the data scatter. · Advice regarding such issues as clustering of "]
[{"$nulls":4},"categorical and mixed scale data, similarity and network data, interpretation aids, anomalous clusters, the number of clusters, etc. · Thorough attention to data-driven modelling including a number of mathematically stated relations between statistical and geometrical concepts including those between goodness-of-fit criteria for decision trees and data standardization, similarity and consensus clustering, modularity clustering and uniform partitioning. New edition highlights: · Inclusion of ranking issues such as Google PageRank, linear stratification and tied rankings median, consensus clustering, semi-average clustering, one-cluster clustering · Restructured to make the logics more straightforward and sections self-contained Core Data Analysis: Summarization, Correlation and Visualization is aimed at those who are eager to participate in developing the field as well as appealing to novices and practitioners. ."]