Modelisation Predictive Et Apprentissage Statistique Avec R Book PDF, EPUB Download & Read Online Free

Modélisation prédictive et apprentissage statistique avec R
Author: TUFFERY Stéphane
Publisher: Editions TECHNIP
ISBN: 2710811588
Pages: 432
Year: 2015-01-02
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Issu de formations devant des publics variés, cet ouvrage présente les principales méthodes de modélisation de statistique et de machine learning, à travers le fil conducteur d’une étude de cas. Chaque méthode fait l’objet d’un rappel de cours et est accompagnée de références bibliographiques, puis est mise en oeuvre avec des explications détaillées sur les calculs effectués, les interprétations des résultats et jusqu’aux astuces de programmation permettant d’optimiser les temps de calcul. À ce sujet, une annexe est consacrée au traitement des données massives. L’ouvrage commence par les méthodes de classement classiques les plus éprouvées, mais aborde rapidement les méthodes plus récentes et avancées : régression ridge, lasso, elastic net, boosting, forêts aléatoires, Extra-Trees, réseaux de neurones, séparateurs à vaste marge. Chaque fois, le lien est fait entre la théorie et les résultats obtenus pour montrer qu’ils illustrent bien les principes sous-jacents à ces méthodes. Mais l’aspect pratique est aussi privilégié, avec l’objectif de permettre au lecteur une mise en oeuvre rapide et efficace dans son travail concret. L’exploration et la préparation préliminaire des données sont d’ailleurs décrites, ainsi que le processus de sélection des variables. Une synthèse finale est faite de toutes les méthodes présentées. La mise en oeuvre s’appuie sur le logiciel libre R et sur un jeu public de données. Ce dernier peut être téléchargé sur internet et présente l’intérêt d’être riche, complet et de permettre des comparaisons grâce aux nombreuses publications dans lesquelles il a servi. Le logiciel statistique utilisé est R, actuellement celui qui se développe le plus : devenu la lingua franca de la statistique et l’outil le plus répandu dans le monde académique, il prend également de plus en plus de place dans le monde de l’entreprise, à tel point que tous les logiciels commerciaux proposent désormais une interface avec R. Outre qu’il est disponible pour tous, dans de multiples environnements, il est aussi le plus riche statistiquement et c’est le seul logiciel permettant de mettre en oeuvre toutes les méthodes présentées dans cet ouvrage. Enfin, son langage de programmation particulièrement élégant et adapté au calcul athématique permet de se concentrer dans le codage sur les aspects statistiques. R permet d’arriver directement à l’essentiel et de mieux comprendre les méthodes exposées dans l’ouvrage. Le Code R utilisé dans l’ouvrage est disponible sur cette page dans la partie "Bonus/lire". Table des matières : Présentation du jeu de données. Préparation des données. Exploration des données. Discrétisation automatique supervisée des variables continues. La régression logistique. La régression logistique pénalisée ridge. La régression logistique pénalisée lasso. La régression logistique PLS. L’arbre de décision CART. L’algorithme PRIM. Les forêts aléatoires. Le bagging. Les forêts aléatoires de modèles logistiques. Le boosting. Les Support Vector Machines. Les réseaux de neurones. Synthèse des méthodes prédictives. Annexes. Bibliographie. Index des packages R utilisés.
Model Choice and Model Aggregation
Author: Frederic Bertrand, Jean-Jacques Droesbeke, Gilbert Saporta
Publisher:
ISBN: 2710811774
Pages: 355
Year: 2017-09-27
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For over forty years, choosing a statistical model thanks to data consisted in optimizing a criterion based on penalized likelihood (H. Akaike, 1973) or penalized least squares (C. Mallows, 1973). These methods are valid for predictive model choice (regression, classification) and for descriptive models (clustering, mixtures). Most of their properties are asymptotic, but a non-asymptotic theory has emerged at the end of the last century (Birge-Massart, 1997). Instead of choosing the best model among several candidates, model aggregation combines different models, often linearly, allowing better predictions. Bayesian statistics provide a useful framework for model choice and model aggregation with Bayesian Model Averaging. In a purely predictive context and with very few assumptions, ensemble methods or meta-algorithms, such as boosting and random forests, have proven their efficiency. This volume originates from the collaboration of high-level specialists: Christophe Biernacki (Universite de Lille I), Jean-Michel Marin (Universite de Montpellier), Pascal Massart (Universite de Paris-Sud), Cathy Maugis-Rabusseau (INSA de Toulouse), Mathilde Mougeot (Universite Paris Diderot), and Nicolas Vayatis (Ecole Normale Superieure de Cachan) who were all speakers at the 16th biennal workshop on advanced statistics organized by the French Statistical Society. In this book, the reader will find a synthesis of the methodologies' foundations and of recent work and applications in various fields. The French Statistical Society (SFdS) is a non-profit organization that promotes the development of statistics, as well as a professional body for all kinds of statisticians working in public and private sectors. Founded in 1997, SFdS is the heir of the Societe de Statistique de Paris, established in 1860. SFdS is a corporate member of the International Statistical Institute and a founding member of FENStatS--the Federation of European National Statistical Societies.
Data Mining and Statistics for Decision Making
Author: Stéphane Tufféry
Publisher: John Wiley & Sons
ISBN: 0470979283
Pages: 716
Year: 2011-03-23
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Data mining is the process of automatically searching large volumes of data for models and patterns using computational techniques from statistics, machine learning and information theory; it is the ideal tool for such an extraction of knowledge. Data mining is usually associated with a business or an organization's need to identify trends and profiles, allowing, for example, retailers to discover patterns on which to base marketing objectives. This book looks at both classical and recent techniques of data mining, such as clustering, discriminant analysis, logistic regression, generalized linear models, regularized regression, PLS regression, decision trees, neural networks, support vector machines, Vapnik theory, naive Bayesian classifier, ensemble learning and detection of association rules. They are discussed along with illustrative examples throughout the book to explain the theory of these methods, as well as their strengths and limitations. Key Features: Presents a comprehensive introduction to all techniques used in data mining and statistical learning, from classical to latest techniques. Starts from basic principles up to advanced concepts. Includes many step-by-step examples with the main software (R, SAS, IBM SPSS) as well as a thorough discussion and comparison of those software. Gives practical tips for data mining implementation to solve real world problems. Looks at a range of tools and applications, such as association rules, web mining and text mining, with a special focus on credit scoring. Supported by an accompanying website hosting datasets and user analysis. Statisticians and business intelligence analysts, students as well as computer science, biology, marketing and financial risk professionals in both commercial and government organizations across all business and industry sectors will benefit from this book.
The R Book
Author: Michael J. Crawley
Publisher: John Wiley & Sons
ISBN: 1118448960
Pages: 1080
Year: 2012-11-07
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Hugely successful and popular text presenting an extensive and comprehensive guide for all R users The R language is recognized as one of the most powerful and flexible statistical software packages, enabling users to apply many statistical techniques that would be impossible without such software to help implement such large data sets. R has become an essential tool for understanding and carrying out research. This edition: Features full colour text and extensive graphics throughout. Introduces a clear structure with numbered section headings to help readers locate information more efficiently. Looks at the evolution of R over the past five years. Features a new chapter on Bayesian Analysis and Meta-Analysis. Presents a fully revised and updated bibliography and reference section. Is supported by an accompanying website allowing examples from the text to be run by the user. Praise for the first edition: ‘…if you are an R user or wannabe R user, this text is the one that should be on your shelf. The breadth of topics covered is unsurpassed when it comes to texts on data analysis in R.’ (The American Statistician, August 2008) ‘The High-level software language of R is setting standards in quantitative analysis. And now anybody can get to grips with it thanks to The R Book…’ (Professional Pensions, July 2007)
The Elements of Statistical Learning
Author: Trevor Hastie, Robert Tibshirani, Jerome Friedman
Publisher: Springer Science & Business Media
ISBN: 0387216065
Pages: 536
Year: 2013-11-11
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During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It should be a valuable resource for statisticians and anyone interested in data mining in science or industry. The book’s coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book. This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. There is also a chapter on methods for “wide” data (p bigger than n), including multiple testing and false discovery rates. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie co-developed much of the statistical modeling software and environment in R/S-PLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, projection pursuit and gradient boosting.
Geographic Data Mining and Knowledge Discovery, Second Edition
Author: Harvey J. Miller, Jiawei Han
Publisher: CRC Press
ISBN: 1420073982
Pages: 486
Year: 2009-05-27
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The Definitive Volume on Cutting-Edge Exploratory Analysis of Massive Spatial and Spatiotemporal Databases Since the publication of the first edition of Geographic Data Mining and Knowledge Discovery, new techniques for geographic data warehousing (GDW), spatial data mining, and geovisualization (GVis) have been developed. In addition, there has been a rise in the use of knowledge discovery techniques due to the increasing collection and storage of data on spatiotemporal processes and mobile objects. Incorporating these novel developments, this second edition reflects the current state of the art in the field. New to the Second Edition Updated material on geographic knowledge discovery (GKD), GDW research, map cubes, spatial dependency, spatial clustering methods, clustering techniques for trajectory data, the INGENS 2.0 software, and GVis techniques New chapter on data quality issues in GKD New chapter that presents a tree-based partition querying methodology for medoid computation in large spatial databases New chapter that discusses the use of geographically weighted regression as an exploratory technique New chapter that gives an integrated approach to multivariate analysis and geovisualization Five new chapters on knowledge discovery from spatiotemporal and mobile objects databases Geographic data mining and knowledge discovery is a promising young discipline with many challenging research problems. This book shows that this area represents an important direction in the development of a new generation of spatial analysis tools for data-rich environments. Exploring various problems and possible solutions, it will motivate researchers to develop new methods and applications in this emerging field.
Oil & Gas Engineering Guide (The) - 2nd ED
Author: BARON Hervé
Publisher: Editions TECHNIP
ISBN: 2710811510
Pages: 270
Year: 2015-03-01
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This book provides the reader with: • a comprehensive description of engineering activities carried out on oil & gas projects, • a description of the work of each engineering discipline, including illustrations of all common documents, • an overall view of the plant design sequence and schedule, • practical tools to manage and control engineering activities. This book is designed to serve as a map to anyone involved with engineering activities. It enables the reader to get immediately oriented in any engineering development, to know which are the critical areas to monitor and the proven methods to apply. It will fulfill the needs of anyone wishing to improve engineering and project execution. Table des matières : 1. Project Engineering. 2. The Design Basis. 3. Process. 4. Equipment/Mechanical. 5. Plant Layout. 6. Safety & Environment. 7. Civil Engineering. 8. Materials & Corrosion. 9. Piping. 10. Plant Model. 11. Instrumentation and Control. 12. Electrical. 13. Off-Shore. 14. The Overall Work Process. 15. BASIC, FEED and Detail Design. 16. Matching the Project Schedule. 17. Engineering Management. 18. Methods & Tools. 19. Field Engineering. 20. Revamping.
Introductory Statistics with R
Author: Peter Dalgaard
Publisher: Springer Science & Business Media
ISBN: 0387790543
Pages: 364
Year: 2008-06-27
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This book provides an elementary-level introduction to R, targeting both non-statistician scientists in various fields and students of statistics. The main mode of presentation is via code examples with liberal commenting of the code and the output, from the computational as well as the statistical viewpoint. Brief sections introduce the statistical methods before they are used. A supplementary R package can be downloaded and contains the data sets. All examples are directly runnable and all graphics in the text are generated from the examples. The statistical methodology covered includes statistical standard distributions, one- and two-sample tests with continuous data, regression analysis, one-and two-way analysis of variance, regression analysis, analysis of tabular data, and sample size calculations. In addition, the last four chapters contain introductions to multiple linear regression analysis, linear models in general, logistic regression, and survival analysis.
Python for Data Analysis
Author: Wes McKinney
Publisher: "O'Reilly Media, Inc."
ISBN: 1491957611
Pages: 550
Year: 2017-09-25
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Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6, the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You’ll learn the latest versions of pandas, NumPy, IPython, and Jupyter in the process. Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It’s ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub. Use the IPython shell and Jupyter notebook for exploratory computing Learn basic and advanced features in NumPy (Numerical Python) Get started with data analysis tools in the pandas library Use flexible tools to load, clean, transform, merge, and reshape data Create informative visualizations with matplotlib Apply the pandas groupby facility to slice, dice, and summarize datasets Analyze and manipulate regular and irregular time series data Learn how to solve real-world data analysis problems with thorough, detailed examples
Geopolitical Atlas of the Oceans
Author: Didier Ortolland, Jean-Pierre Pirat
Publisher:
ISBN: 2710811642
Pages: 352
Year: 2017-09-30
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White not providing an answer to all maritime problems, the 1982 Convention on the Law of the Sea has successfully solved a number of issues relating to the exercise by States of their sovereignty and jurisdiction over ocean space and its resources. Among its major achievements are the adoption of clear limits of jurisdiction of coastal States over ocean areas and their concomitant rules of navigation; basic guidelines for the use of ocean resources and the creation of institutions, in particular for the exploitation of deep seabed resources and the peaceful settlement of disputes. After centuries of divergent practices, all coastal States have agreed to adopt a uniform limit for their territorial sea at 12 nautical miles. As a result, more than 100 straits used for international navigation have fallen under national sovereignty, thus leading to the adoption of a new regime of transit passage. Beyond the territorial sea, States can establish, whenever possible, an exclusive economic zone to a maximum extent of 200 miles. Such an extension may lead to conflicting claims. Similarly, the definition of the continental shelf as the natural prolongation of land territory under the sea, either arbitrarily fixed at 200 miles (in the absence of a shelf) or extending up to the limit of the continental margin, has led to a second phase of appropriation of maritime spaces by certain coastal States. Finally, as far as the deep seabed beyond the limits of national jurisdiction is concerned, its resources will have to be exploited under the control of the International Seabed Authority established in Jamaica. Within this legal framework, the sea remains more than ever a source of wealth and becomes increasingly an area of conflicts. National geopolitical considerations push States to adopt specific maritime policies generating tensions and conflicts. These are mainly the result of national political and economic ambitions. Fishery resources are becoming scarce, offshore oil and gas production is still essential for the energy balance of nations and possibilities of deep seabed mineral resource exploitation are getting closer. In addition, at 80% of the international trade volume, maritime transport remains the backbone of globalization. Besides, seaborne piracy remains a significant issue and the respect for freedom of navigation through international straits is becoming increasingly important. White some conflicting claims become more acute, some apparently frozen maritime disputes remain worrying. This is the case for example of Greece and Turkey in the Aegean Sea, and Colombia and Venezuela in the Gulf of Venezuela. The situation remains confused in some parts of the Persian Gulf, the waters of which are particularly rich in oil and gas, or off the coast of Africa. Asia also offers a wide range of unresolved maritime conflicts that are increasingly upsetting regional and international stability. It is against the backdrop of these alarming circumstances that this Atlas endeavors to present the various components of present maritime geopolitics. This publication deals with the major issues relating to maritime spaces and their delimitations, navigation and security, international straits and seabed resources. As such, it should represent an essential tool for the understanding of States' ocean policies and governmental stances.
Modeling Psychophysical Data in R
Author: Kenneth Knoblauch, Laurence T. Maloney
Publisher: Springer Science & Business Media
ISBN: 1461444756
Pages: 365
Year: 2012-09-02
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Many of the commonly used methods for modeling and fitting psychophysical data are special cases of statistical procedures of great power and generality, notably the Generalized Linear Model (GLM). This book illustrates how to fit data from a variety of psychophysical paradigms using modern statistical methods and the statistical language R. The paradigms include signal detection theory, psychometric function fitting, classification images and more. In two chapters, recently developed methods for scaling appearance, maximum likelihood difference scaling and maximum likelihood conjoint measurement are examined. The authors also consider the application of mixed-effects models to psychophysical data. R is an open-source programming language that is widely used by statisticians and is seeing enormous growth in its application to data in all fields. It is interactive, containing many powerful facilities for optimization, model evaluation, model selection, and graphical display of data. The reader who fits data in R can readily make use of these methods. The researcher who uses R to fit and model his data has access to most recently developed statistical methods. This book does not assume that the reader is familiar with R, and a little experience with any programming language is all that is needed to appreciate this book. There are large numbers of examples of R in the text and the source code for all examples is available in an R package MPDiR available through R. Kenneth Knoblauch is a researcher in the Department of Integrative Neurosciences in Inserm Unit 846, The Stem Cell and Brain Research Institute and associated with the University Claude Bernard, Lyon 1, in France. Laurence T. Maloney is Professor of Psychology and Neural Science at New York University. His research focusses on applications of mathematical models to perception, motor control and decision making.
Empirical model-building and response surfaces
Author: George E. P. Box, Norman Richard Draper
Publisher: John Wiley & Sons Inc
ISBN:
Pages: 669
Year: 1987-01-16
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An innovative discussion of building empirical models and the fitting of surfaces to data. Introduces the general philosophy of response surface methodology, and details least squares for response surface work, factorial designs at two levels, fitting second-order models, adequacy of estimation and the use of transformation, occurrence and elucidation of ridge systems, and more. Some results are presented for the first time. Includes real-life exercises, nearly all with solutions.
Handbook of Uncertainty Quantification
Author: Roger Ghanem, David Higdon, Howman Owhadi
Publisher: Springer
ISBN: 331912384X
Pages: 1000
Year: 2016-05-08
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The topic of Uncertainty Quantification (UQ) has witnessed massive developments in response to the promise of achieving risk mitigation through scientific prediction. It has led to the integration of ideas from mathematics, statistics and engineering being used to lend credence to predictive assessments of risk but also to design actions (by engineers, scientists and investors) that are consistent with risk aversion. The objective of this Handbook is to facilitate the dissemination of the forefront of UQ ideas to their audiences. We recognize that these audiences are varied, with interests ranging from theory to application, and from research to development and even execution.
History of the Theatre
Author: Oscar Gross Brockett, Franklin Joseph Hildy
Publisher: Pearson College Division
ISBN: 0205358780
Pages: 692
Year: 2003
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Chronicles the evolution of the theater from its beginnings to the early twenty-first century, covering styles, creative and technical elements, and the theater's impact on society and culture. Focuses largely on Europe and the U.S. but also discusses Africa, Asia, Latin America, Canada, Australia, and New Zealand.
Drilling Data Handbook 7th
Author: Gilles Gabolde, Jean-Paul Nguyen
Publisher: Editions TECHNIP
ISBN: 2710808714
Pages: 576
Year: 2006-01-01
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The seventh edition of the Drilling Data Handbook was published in 1999. We are in a new communication techniques have considerably evolved. The electronic hardware and soft communication anywhere in the world, access to huge databases, as well as permanent documents required by the drilling personnel. At the moment of making a decision about Drilling Data Handbook, the question was: is it pertinent to do an electronic version on accessible one with a connection to different sites, or to keep the popular concept of the people have been using it for decades? The Internet gives access to an infinite volume everybody has experimented the trouble of being lost in the way, or the difficulty to read information. The Drilling Data Handbook does not want to compete with the web sites on other sources of electronic documentation. The main goal of our contribution to the drill access very quickly and without any additional resources to the fundamental data at the floor. That is the reason why we made the decision to present you this reviewed and up the formula you are familiar with, and we hope that it will continue to help you when play well.

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