الفهرس الالي لمكتبة كلية العلوم الدقيقة و الاعلام الالي
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Applied statistics using the computer / Ronald S. King
Titre : Applied statistics using the computer Type de document : texte imprimé Auteurs : Ronald S. King ; Bryant Julstrom Editeur : Sherman Oaks, CA : Alfred Pub. Co. Année de publication : 1982 Importance : xv, 477 p. Présentation : ill. Format : 26 cm ISBN/ISSN/EAN : 978-0-88284-174-8 Note générale : Distributor from label on t.p. Includes index. Langues : Anglais (eng) Mots-clés : Computer using applied statistics Index. décimale : 519 Résumé : Distributor from label on t.p. Includes index. Applied statistics using the computer [texte imprimé] / Ronald S. King ; Bryant Julstrom . - Sherman Oaks, CA : Alfred Pub. Co., 1982 . - xv, 477 p. : ill. ; 26 cm.
ISBN : 978-0-88284-174-8
Distributor from label on t.p. Includes index.
Langues : Anglais (eng)
Mots-clés : Computer using applied statistics Index. décimale : 519 Résumé : Distributor from label on t.p. Includes index. Réservation
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Code-barres Cote Support Localisation Section Disponibilité fsei00298 519-69.1 Ouvrage Faculté des Sciences Exactes et Informatique 500 - Sciences de la nature et Mathématiques Disponible Big data analytics
Titre : Big data analytics : handbook of statistics volume 33 Type de document : texte imprimé Auteurs : Venugopal Govindaraju, Editeur scientifique ; Vijay V. Raghavan, Editeur scientifique ; Calyampudi Radhakrishna Rao (1920-....), Editeur scientifique Editeur : Amsterdam : North-Holland Année de publication : cop. 2015 Autre Editeur : Elsevier Collection : Handbook of Statistics num. 33 Importance : 1 ressource dématérialisée ISBN/ISSN/EAN : 978-0-444-63492-4 Note générale : Bibliogr. en fin de chapitres. Index Langues : Anglais (eng) Mots-clés : Big data analytics handbook statistics Index. décimale : 515 Résumé : While the term Big Data is open to varying interpretation, it is quite clear that the Volume, Velocity, and Variety (3Vs) of data have impacted every aspect of computational science and its applications. The volume of data is increasing at a phenomenal rate and a majority of it is unstructured. With big data, the volume is so large that processing it using traditional database and software techniques is difficult, if not impossible. The drivers are the ubiquitous sensors, devices, social networks and the all-pervasive web. Scientists are increasingly looking to derive insights from the massive quantity of data to create new knowledge. In common usage, Big Data has come to refer simply to the use of predictive analytics or other certain advanced methods to extract value from data, without any required magnitude thereon. Challenges include analysis, capture, curation, search, sharing, storage, transfer, visualization, and information privacy. While there are challenges, there are huge opportunities emerging in the fields of Machine Learning, Data Mining, Statistics, Human-Computer Interfaces and Distributed Systems to address ways to analyze and reason with this data. The edited volume focuses on the challenges and opportunities posed by "Big Data" in a variety of domains and how statistical techniques and innovative algorithms can help glean insights and accelerate discovery. Big data has the potential to help companies improve operations and make faster, more intelligent decisions.
Review of big data research challenges from diverse areas of scientific endeavor
Rich perspective on a range of data science issues from leading researchers
Insight into the mathematical and statistical theory underlying the computational methods used to address big data analytics problems in a variety of domainsBig data analytics : handbook of statistics volume 33 [texte imprimé] / Venugopal Govindaraju, Editeur scientifique ; Vijay V. Raghavan, Editeur scientifique ; Calyampudi Radhakrishna Rao (1920-....), Editeur scientifique . - Amsterdam : North-Holland : [S.l.] : Elsevier, cop. 2015 . - 1 ressource dématérialisée. - (Handbook of Statistics; 33) .
ISBN : 978-0-444-63492-4
Bibliogr. en fin de chapitres. Index
Langues : Anglais (eng)
Mots-clés : Big data analytics handbook statistics Index. décimale : 515 Résumé : While the term Big Data is open to varying interpretation, it is quite clear that the Volume, Velocity, and Variety (3Vs) of data have impacted every aspect of computational science and its applications. The volume of data is increasing at a phenomenal rate and a majority of it is unstructured. With big data, the volume is so large that processing it using traditional database and software techniques is difficult, if not impossible. The drivers are the ubiquitous sensors, devices, social networks and the all-pervasive web. Scientists are increasingly looking to derive insights from the massive quantity of data to create new knowledge. In common usage, Big Data has come to refer simply to the use of predictive analytics or other certain advanced methods to extract value from data, without any required magnitude thereon. Challenges include analysis, capture, curation, search, sharing, storage, transfer, visualization, and information privacy. While there are challenges, there are huge opportunities emerging in the fields of Machine Learning, Data Mining, Statistics, Human-Computer Interfaces and Distributed Systems to address ways to analyze and reason with this data. The edited volume focuses on the challenges and opportunities posed by "Big Data" in a variety of domains and how statistical techniques and innovative algorithms can help glean insights and accelerate discovery. Big data has the potential to help companies improve operations and make faster, more intelligent decisions.
Review of big data research challenges from diverse areas of scientific endeavor
Rich perspective on a range of data science issues from leading researchers
Insight into the mathematical and statistical theory underlying the computational methods used to address big data analytics problems in a variety of domainsRéservation
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Code-barres Cote Support Localisation Section Disponibilité fsei12053 515-208.1 Ouvrage Faculté des Sciences Exactes et Informatique 500 - Sciences de la nature et Mathématiques Disponible Chemometrics: Statistics and Computer Application in Analytical Chemistry / Matthias Otto
Titre : Chemometrics: Statistics and Computer Application in Analytical Chemistry Type de document : texte imprimé Auteurs : Matthias Otto, Auteur Mention d'édition : 3em ed. Editeur : Weinheim : Wiley-VCH Année de publication : 2007 Importance : 328 p. Format : 25 x 17 cm. ISBN/ISSN/EAN : 978-3-527-31418-8 Langues : Français (fre) Mots-clés : Chemometrics Statistics Computer Application Analytical Chemistry Résumé : The third edition of this long-selling introductory textbook and ready reference covers all pertinent topics, from basic statistics via modeling and databases right up to the latest regulatory issues.
The experienced and internationally recognized author, Matthias Otto, introduces the statistical-mathematical evaluation of chemical measurements, especially analytical ones, going on to provide a modern approach to signal processing, designing and optimizing experiments, pattern recognition and classification, as well as modeling simple and nonlinear relationships. Analytical databases are equally covered as are applications of multiway analysis, artificial intelligence, fuzzy theory, neural networks, and genetic algorithms. The new edition has 10% new content to cover such recent developments as orthogonal signal correction and new data exchange formats, tree based classification and regression, independent component analysis, ensemble methods and neuro-fuzzy systems. It still retains, however, the proven features from previous editions: worked examples, questions and problems, additional information and brief explanations in the margin.Chemometrics: Statistics and Computer Application in Analytical Chemistry [texte imprimé] / Matthias Otto, Auteur . - 3em ed. . - Weinheim : Wiley-VCH, 2007 . - 328 p. ; 25 x 17 cm.
ISBN : 978-3-527-31418-8
Langues : Français (fre)
Mots-clés : Chemometrics Statistics Computer Application Analytical Chemistry Résumé : The third edition of this long-selling introductory textbook and ready reference covers all pertinent topics, from basic statistics via modeling and databases right up to the latest regulatory issues.
The experienced and internationally recognized author, Matthias Otto, introduces the statistical-mathematical evaluation of chemical measurements, especially analytical ones, going on to provide a modern approach to signal processing, designing and optimizing experiments, pattern recognition and classification, as well as modeling simple and nonlinear relationships. Analytical databases are equally covered as are applications of multiway analysis, artificial intelligence, fuzzy theory, neural networks, and genetic algorithms. The new edition has 10% new content to cover such recent developments as orthogonal signal correction and new data exchange formats, tree based classification and regression, independent component analysis, ensemble methods and neuro-fuzzy systems. It still retains, however, the proven features from previous editions: worked examples, questions and problems, additional information and brief explanations in the margin.Réservation
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Code-barres Cote Support Localisation Section Disponibilité fsei11820 543-28.1 Ouvrage Faculté des Sciences Exactes et Informatique 500 - Sciences de la nature et Mathématiques Disponible fsei11821 543-28.2 Ouvrage Faculté des Sciences Exactes et Informatique 500 - Sciences de la nature et Mathématiques Disponible fsei11822 543-28.3 Ouvrage Faculté des Sciences Exactes et Informatique 500 - Sciences de la nature et Mathématiques Disponible Time series modeling for analysis and control / Kohei Ohtsu
Titre : Time series modeling for analysis and control : advanced autopilot and monitoring systems Type de document : texte imprimé Auteurs : Kohei Ohtsu ; Hui Peng ; G. Kitagawa Importance : 1 online resource (ix, 119 pages) Présentation : illustrations (some color ISBN/ISSN/EAN : 978-4-431-55303-8 Mots-clés : toegepaste statistiek applied statistics statistiek statistics statistische analyse statistical analysis Statistics (General) Statistiek (algemeen) Index. décimale : 519 Résumé : This book presents multivariate time series methods for the analysis and optimal control of feedback systems. Although ships? autopilot systems are considered through the entire book, the methods set forth in this book can be applied to many other complicated, large, or noisy feedback control systems for which it is difficult to derive a model of the entire system based on theory in that subject area. The basic models used in this method are the multivariate autoregressive model with exogenous variables (ARX) model and the radial bases function net-type coefficients ARX model. The noise contribution analysis can then be performed through the estimated autoregressive (AR) model and various types of autopilot systems can be designed through the state?space representation of the models. The marine autopilot systems addressed in this book include optimal controllers for course-keeping motion, rolling reduction controllers with rudder motion, engine governor controllers, noise adaptive autopilots, route-tracking controllers by direct steering, and the reference course-setting approach. The methods presented here are exemplified with real data analysis and experiments on real ships. This book is highly recommended to readers who are interested in designing optimal or adaptive controllers not only of ships but also of any other complicated systems under noisy disturbance conditions. Time series modeling for analysis and control : advanced autopilot and monitoring systems [texte imprimé] / Kohei Ohtsu ; Hui Peng ; G. Kitagawa . - [s.d.] . - 1 online resource (ix, 119 pages) : illustrations (some color.
ISBN : 978-4-431-55303-8
Mots-clés : toegepaste statistiek applied statistics statistiek statistics statistische analyse statistical analysis Statistics (General) Statistiek (algemeen) Index. décimale : 519 Résumé : This book presents multivariate time series methods for the analysis and optimal control of feedback systems. Although ships? autopilot systems are considered through the entire book, the methods set forth in this book can be applied to many other complicated, large, or noisy feedback control systems for which it is difficult to derive a model of the entire system based on theory in that subject area. The basic models used in this method are the multivariate autoregressive model with exogenous variables (ARX) model and the radial bases function net-type coefficients ARX model. The noise contribution analysis can then be performed through the estimated autoregressive (AR) model and various types of autopilot systems can be designed through the state?space representation of the models. The marine autopilot systems addressed in this book include optimal controllers for course-keeping motion, rolling reduction controllers with rudder motion, engine governor controllers, noise adaptive autopilots, route-tracking controllers by direct steering, and the reference course-setting approach. The methods presented here are exemplified with real data analysis and experiments on real ships. This book is highly recommended to readers who are interested in designing optimal or adaptive controllers not only of ships but also of any other complicated systems under noisy disturbance conditions. Réservation
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Code-barres Cote Support Localisation Section Disponibilité fsei12331 519-72.1 Ouvrage Faculté des Sciences Exactes et Informatique 500 - Sciences de la nature et Mathématiques Disponible fsei12332 519-72.2 Ouvrage Faculté des Sciences Exactes et Informatique 500 - Sciences de la nature et Mathématiques Disponible