statistical learning

Statistical Learning and Modeling in Data Analysis Methods and Applications【電子書籍】Machine Learning A Practical Approach on the Statistical Learning Theory【電子書籍】 Moacir Antonelli PontiStatistical Learning from a Regression Perspective【電子書籍】 Richard A. BerkStatistical Modeling in Machine Learning Concepts and Applications【電子書籍】Data Science and Machine Learning Mathematical and Statistical Methods【電子書籍】 Dirk P. KroeseHandbook of Computational Social Science, Volume 2 Data Science, Statistical Modelling, and Machine Learning Methods【電子書籍】(出版社)Springer-Verlag New York An Introduction to Statistical Learning 1冊 978-1-4614-7137-0Statistical Modeling and Machine Learning for Molecular Biology【電子書籍】 Alan Moses(出版社)Chapman & Hall/CRC Statistical Reinforcement Learning 1冊 978-1-4398-5689-5Ultimate Parallel and Distributed Computing with Julia For Data Science Excel in Data Analysis, Statistical Modeling and Machine Learning by leveraging MLBase.jl and MLJ.jl to optimize workflows (English Edition)【電子書籍】 Nabanita DashAn Introduction to Statistical Learning with Applications in R【電子書籍】 Gareth James(出版社)Springer-Verlag New York The Elements of Statistical Learning 1冊 978-0-387-84857-0洋書 Connectionist, Statistical and Symbolic Approaches to Learning for Natural Language Processing (Lecture Notes in Computer Science)Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data, Third Edition【電子書籍】 Bruce RatnerUP81-015 Springer Neural Networks and Statistical Learning 2014 Ke-LinDu 50MaDAlgebraic Geometry and Statistical Learning Theory【電子書籍】 Sumio Watanabe(出版社)CRC Press Statistical Learning with Sparsity 1冊 978-1-4987-1216-3An Introduction to Statistical Learning: With Applications in R INTRO TO STATISTICAL LEARNING (Springer Texts in Statistics) Gareth James洋書 Paperback, Introduction to Statistical Machine Learning洋書 Routledge Paperback, The Mental Health Diagnostic Desk Reference: Visual Guides and More for Learning to Use the Diagnostic and Statistical Manual (DSM-IV-TR)
 

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  • <p>The contributions gathered in this book focus on modern methods for statistical learning and modeling in data analysis and present a series of engaging real-world applications. The book covers numerous research topics, ranging from statistical inference and modeling to clustering and factorial methods, from directional data analysis to time series analysis and small area estimation. The applications reflect new analyses in a variety of fields, including medicine, finance, engineering, marketing and cyber risk.</p> <p>The book gathers selected and peer-reviewed contributions presented at the 12th Scientific Meeting of the Classification and Data Analysis Group of the Italian Statistical Society (CLADAG 2019), held in Cassino, Italy, on September 11?13, 2019. CLADAG promotes advanced methodological research in multivariate statistics with a special focus on data analysis and classification, and supports the exchange and dissemination of ideas, methodological concepts, numeric...
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  • <p>This book presents the Statistical Learning Theory in a detailed and easy to understand way, by using practical examples, algorithms and source codes. It can be used as a textbook in graduation or undergraduation courses, for self-learners, or as reference with respect to the main theoretical concepts of Machine Learning. Fundamental concepts of Linear Algebra and Optimization applied to Machine Learning are provided, as well as source codes in R, making the book as self-contained as possible.</p> <p>It starts with an introduction to Machine Learning concepts and algorithms such as the Perceptron, Multilayer Perceptron and the Distance-Weighted Nearest Neighbors with examples, in order to provide the necessary foundation so the reader is able to understand the Bias-Variance Dilemma, which is the central point of the Statistical Learning Theory.</p> <p>Afterwards, we introduce all assumptions and formalize the Statistical Learning Theory, allowing the practical study of ...
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  • <p>This textbook considers statistical learning applications when interest centers on the conditional distribution of the response variable, given a set of predictors, and when it is important to characterize how the predictors are related to the response.</p> <p>This fully revised new edition includes important developments over the past 8 years. Consistent with modern data analytics, it emphasizes that a proper statistical learning data analysis derives from sound data collection, intelligent data management, appropriate statistical procedures, and an accessible interpretation of results. As in the first edition, a unifying theme is supervised learning that can be treated as a form of regression analysis. Key concepts and procedures are illustrated with real applications, especially those with practical implications.</p> <p>The material is written for upper undergraduate level and graduate students in the social and life sciences and for researchers who want to apply sta...
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  • <p><em>Statistical Modeling in Machine Learning: Concepts and Applications</em> presents the basic concepts and roles of statistics, exploratory data analysis and machine learning. The various aspects of Machine Learning are discussed along with basics of statistics. Concepts are presented with simple examples and graphical representation for better understanding of techniques. This book takes a holistic approach ? putting key concepts together with an in-depth treatise on multi-disciplinary applications of machine learning. New case studies and research problem statements are discussed, which will help researchers in their application areas based on the concepts of statistics and machine learning.</p> <p><em>Statistical Modeling in Machine Learning: Concepts and Applications</em> will help statisticians, machine learning practitioners and programmers solving various tasks such as classification, regression, clustering, forecasting, recommending and more.</p> <ul> ...
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  • <p>"This textbook is a well-rounded, rigorous, and informative work presenting the mathematics behind modern machine learning techniques. It hits all the right notes: the choice of topics is up-to-date and perfect for a course on data science for mathematics students at the advanced undergraduate or early graduate level. This book fills a sorely-needed gap in the existing literature by not sacrificing depth for breadth, presenting proofs of major theorems and subsequent derivations, as well as providing a copious amount of Python code. I only wish a book like this had been around when I first began my journey!" <strong>-Nicholas Hoell, University of Toronto</strong></p> <p><em>"This is a well-written book that provides a deeper dive into data-scientific methods than many introductory texts. The writing is clear, and the text logically builds up regularization, classification, and decision trees. Compared to its probable competitors, it carves out a unique niche.</em> ...
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  • <p>The <em>Handbook of Computational Social Science</em> is a comprehensive reference source for scholars across multiple disciplines. It outlines key debates in the field, showcasing novel statistical modeling and machine learning methods, and draws from specific case studies to demonstrate the opportunities and challenges in CSS approaches.</p> <p>The Handbook is divided into two volumes written by outstanding, internationally renowned scholars in the field. This second volume focuses on foundations and advances in data science, statistical modeling, and machine learning. It covers a range of key issues, including the management of big data in terms of record linkage, streaming, and missing data. Machine learning, agent-based and statistical modeling, as well as data quality in relation to digital trace and textual data, as well as probability, non-probability, and crowdsourced samples represent further foci. The volume not only makes major contributions to the consolida...
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  • (出版社)Springer-Verlag New York An Introduction to Statistical Learning 1冊●著者:James, Gareth/Witten, Daniela/Hastie, Trevor/Tibshirani, Robert●シリーズ名:Springer Texts in Statistics●Vol.103●頁数他:430 p.●装丁:Hard●版次:1st ed. 2013, Corr. 6th printing 2016●出版社:Springer-Verlag New York●発行日:2016/6/13
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  • <p>Molecular biologists are performing increasingly large and complicated experiments, but often have little background in data analysis. The book is devoted to teaching the statistical and computational techniques molecular biologists need to analyze their data. It explains the big-picture concepts in data analysis using a wide variety of real-world molecular biological examples such as eQTLs, ortholog identification, motif finding, inference of population structure, protein fold prediction and many more. The book takes a pragmatic approach, focusing on techniques that are based on elegant mathematics yet are the simplest to explain to scientists with little background in computers and statistics.</p>画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、楽天kobo商品ページからお願いします。※切り替わらない場合は、こちら をクリックして下さい。 ※このページからは注文できません。
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  • (出版社)Chapman & Hall/CRC Statistical Reinforcement Learning 1冊●著者:Sugiyama, Masashi●頁数他:206 p.●装丁:Hard●出版社:Chapman & Hall/CRC●発行日:2015/6/5
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  • <p>Unleash Julia’s power: Code Your Data Stories, Shape Machine Intelligence!<br /> Book DescriptionThis book takes you through a step-by-step learning journey, starting with the essentials of Julia's syntax, variables, and functions. You'll unlock the power of efficient data handling by leveraging Julia arrays and DataFrames.jl for insightful analysis. Develop expertise in both basic and advanced statistical models, providing a robust toolkit for deriving meaningful data-driven insights. The journey continues with machine learning proficiency, where you'll implement algorithms confidently using MLJ.jl and MLBase.jl, paving the way for advanced data-driven solutions. Explore the realm of Bayesian inference skills through practical applications using Turing.jl, enhancing your ability to extract valuable insights. The book also introduces crucial Julia packages such as Plots.jl for visualizing data and results.<br /> The handbook culminates in optimizing workflows with Julia's p...
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  • <p><strong>An Introduction to Statistical Learning</strong> provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform.</p>...
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  • (出版社)Springer-Verlag New York The Elements of Statistical Learning 1冊●著者:Hastie, Trevor/Tibshirani, Robert/Friedman, Jerome●シリーズ名:Springer Series in Statistics●和文:統計的学習要論 第2版●頁数他:xxii, 745 p.●装丁:Hard●版次:2nd ed.●出版社:Springer-Verlag New York●発行日:2011/4/12
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  • *** We ship internationally, so do not use a package forwarding service. We cannot ship to a package forwarding company address because of the Japanese customs regulation. If it is shipped and customs office does not let the package go, we do not make a refund. 【注意事項】 *** 特に注意してください。 *** ・個人ではない法人・団体名義での購入はできません。この場合税関で滅却されてもお客様負担になりますので御了承願います。 ・お名前にカタカナが入っている場合法人である可能性が高いため当店システムから自動保留します。カタカナで記載が必要な場合はカタカナ変わりローマ字で記載してください。 ・お名前またはご住所が法人・団体名義(XX株式会社等)、商店名などを含めている場合、または電話番号が個人のものではない場合、税関から法人名義でみなされますのでご注意ください。 ・転送サービス会社への発送もできません。この場合税関で滅却されてもお客様負担になりますので御了承願います。 *** ・注文後品切れや価格変動でキャンセルされる場合がございますので予めご了承願います。 ・当店でご購入された商品は、原則として、「個...
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  • <p>Interest in predictive analytics of big data has grown exponentially in the four years since the publication of <strong>Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data, Second Edition. In the third edition of this bestseller,</strong> the author has completely revised, reorganized, and repositioned the original chapters and produced 13 new chapters of creative and useful machine-learning data mining techniques. In sum, the 43 chapters of simple yet insightful quantitative techniques make this book unique in the field of data mining literature.</p> <p>What is new in the Third Edition:</p> <ul> <li></li> <li>The current chapters have been completely rewritten.</li> <li></li> <li>The core content has been extended with strategies and methods for problems drawn from the top predictive analytics conference and statistical modeling workshops.</li> <li></li> <li>Adds thirteen new chapter...
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  • 【30日間返品保証】商品説明に誤りがある場合は、無条件で弊社送料負担で商品到着後30日間返品を承ります。ご満足のいく取引となるよう精一杯対応させていただきます。【インボイス制度対応済み】当社ではインボイス制度に対応した適格請求書発行事業者番号(通称:T番号・登録番号)を印字した納品書(明細書)を商品に同梱してお送りしております。こちらをご利用いただくことで、税務申告時や確定申告時に消費税額控除を受けることが可能になります。また、適格請求書発行事業者番号の入った領収書・請求書をご注文履歴からダウンロードして頂くこともできます(宛名はご希望のものを入力して頂けます)。■商品名■Springer Neural Networks and Statistical Learning 2014 Ke-LinDu■出版社■Springer■著者■Ke-Lin Du■発行年■2014■教科■機械工学 洋書■書き込み■鉛筆や色ペンによる書き込みが少しあります。※書き込みの記載には多少の誤差や見落としがある場合もございます。予めご了承お願い致します。※テキストとプリントのセット商品の場合、書き込みの記載はテキストのみが対象となります。付属品のプリントは実際に使用されたものであり、書き込みがある場合もござい...
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  • <p>Sure to be influential, this book lays the foundations for the use of algebraic geometry in statistical learning theory. Many widely used statistical models and learning machines applied to information science have a parameter space that is singular: mixture models, neural networks, HMMs, Bayesian networks, and stochastic context-free grammars are major examples. Algebraic geometry and singularity theory provide the necessary tools for studying such non-smooth models. Four main formulas are established: 1. the log likelihood function can be given a common standard form using resolution of singularities, even applied to more complex models; 2. the asymptotic behaviour of the marginal likelihood or 'the evidence' is derived based on zeta function theory; 3. new methods are derived to estimate the generalization errors in Bayes and Gibbs estimations from training errors; 4. the generalization errors of maximum likelihood and a posteriori methods are clarified by empirical process ...
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  • (出版社)CRC Press Statistical Learning with Sparsity 1冊●著者:Hastie, Trevor/Tibshirani, Robert/Wainwright, Martin●シリーズ名:Chapman & Hall/CRC Monographs on Statistics & Applied Probability●頁数他:367 p.●装丁:Hard●出版社:CRC Press●発行日:2015/6/8
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  • INTRO TO STATISTICAL LEARNING Springer Texts in Statistics Gareth James Daniela Witten Trevor Hastie SPRINGER NATURE2021 Hardcover 2021 English ISBN:9781071614174 洋書 Computers & Science(コンピューター&科学) Mathematics
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  • *** We ship internationally, so do not use a package forwarding service. We cannot ship to a package forwarding company address because of the Japanese customs regulation. If it is shipped and customs office does not let the package go, we do not make a refund. 【注意事項】 *** 特に注意してください。 *** ・個人ではない法人・団体名義での購入はできません。この場合税関で滅却されてもお客様負担になりますので御了承願います。 ・お名前にカタカナが入っている場合法人である可能性が高いため当店システムから自動保留します。カタカナで記載が必要な場合はカタカナ変わりローマ字で記載してください。 ・お名前またはご住所が法人・団体名義(XX株式会社等)、商店名などを含めている場合、または電話番号が個人のものではない場合、税関から法人名義でみなされますのでご注意ください。 ・転送サービス会社への発送もできません。この場合税関で滅却されてもお客様負担になりますので御了承願います。 *** ・注文後品切れや価格変動でキャンセルされる場合がございますので予めご了承願います。 ・当店でご購入された商品は、原則として、「個...
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  • *** We ship internationally, so do not use a package forwarding service. We cannot ship to a package forwarding company address because of the Japanese customs regulation. If it is shipped and customs office does not let the package go, we do not make a refund. 【注意事項】 *** 特に注意してください。 *** ・個人ではない法人・団体名義での購入はできません。この場合税関で滅却されてもお客様負担になりますので御了承願います。 ・お名前にカタカナが入っている場合法人である可能性が高いため当店システムから自動保留します。カタカナで記載が必要な場合はカタカナ変わりローマ字で記載してください。 ・お名前またはご住所が法人・団体名義(XX株式会社等)、商店名などを含めている場合、または電話番号が個人のものではない場合、税関から法人名義でみなされますのでご注意ください。 ・転送サービス会社への発送もできません。この場合税関で滅却されてもお客様負担になりますので御了承願います。 *** ・注文後品切れや価格変動でキャンセルされる場合がございますので予めご了承願います。 ・当店でご購入された商品は、原則として、「個...
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