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Privacy in Statistical Databases, Domingo-Ferrer


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Цена: 46570.00T
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Склад Америка: 152 шт.  
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Автор: Domingo-Ferrer
Название:  Privacy in Statistical Databases
ISBN: 9783031139444
Издательство: Springer
Классификация:







ISBN-10: 3031139445
Обложка/Формат: Soft cover
Страницы: 376
Вес: 0.59 кг.
Дата издания: 19.08.2022
Серия: Lecture Notes in Computer Science
Язык: English
Издание: 1st ed. 2022
Иллюстрации: 66 illustrations, color; 32 illustrations, black and white; xi, 376 p. 98 illus., 66 illus. in color.
Размер: 235 x 155
Читательская аудитория: Professional & vocational
Основная тема: Computer Science
Подзаголовок: International conference, psd 2022, paris, france, september 21-23, 2022, proceedings
Ссылка на Издательство: Link
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Поставляется из: Германии
Описание: This book constitutes the refereed proceedings of the International Conference on Privacy in Statistical Databases, PSD 2022, held in Paris, France, during September 21-23, 2022. The 25 papers presented in this volume were carefully reviewed and selected from 45 submissions. They were organized in topical sections as follows: Privacy models; tabular data; disclosure risk assessment and record linkage; privacy-preserving protocols; unstructured and mobility data; synthetic data; machine learning and privacy; and case studies.
Дополнительное описание: Privacy models.- An optimization-based decomposition heuristic for the microaggregation problem.- Privacy Analysis with a Distributed Transition System and a data-wise metric.- Multivariate Mean Comparison under Differential Privacy.- Asking The Proper Qu


The Elements of Statistical Learning

Автор: Trevor Hastie; Robert Tibshirani; Jerome Friedman
Название: The Elements of Statistical Learning
ISBN: 0387848576 ISBN-13(EAN): 9780387848570
Издательство: Springer
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Цена: 69870.00 T
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Описание: This major new edition features many topics not covered in the original, including graphical models, random forests, and ensemble methods. As before, it covers the conceptual framework for statistical data in our rapidly expanding computerized world.

Computer Age Statistical Inference, Student Edition

Автор: Bradley Efron , Trevor Hastie
Название: Computer Age Statistical Inference, Student Edition
ISBN: 1108823416 ISBN-13(EAN): 9781108823418
Издательство: Cambridge Academ
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Цена: 33790.00 T
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Описание: Computing power has revolutionized the theory and practice of statistical inference. Now in paperback, and fortified with 130 class-tested exercises, this book explains modern statistical thinking from classical theories to state-of-the-art prediction algorithms. Anyone who applies statistical methods to data will value this landmark text.

Mining of Massive Datasets

Автор: Leskovec Jure
Название: Mining of Massive Datasets
ISBN: 1108476341 ISBN-13(EAN): 9781108476348
Издательство: Cambridge Academ
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Цена: 71810.00 T
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Описание: Essential reading for students and practitioners, this book focuses on practical algorithms used to solve key problems in data mining, with exercises suitable for students from the advanced undergraduate level and beyond. This third edition includes new and extended coverage on decision trees, deep learning, and mining social-network graphs.

Handbook of Statistical Analysis and Data Mining Applications, 2 ed.

Автор: Robert Nisbet , Gary Miner, Ken Yale
Название: Handbook of Statistical Analysis and Data Mining Applications, 2 ed.
ISBN: 0124166326 ISBN-13(EAN): 9780124166325
Издательство: Elsevier Science
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Цена: 88690.00 T
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Описание:

Handbook of Statistical Analysis and Data Mining Applications, Second Edition, is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers, both academic and industrial, through all stages of data analysis, model building and implementation. The handbook helps users discern technical and business problems, understand the strengths and weaknesses of modern data mining algorithms and employ the right statistical methods for practical application.

This book is an ideal reference for users who want to address massive and complex datasets with novel statistical approaches and be able to objectively evaluate analyses and solutions. It has clear, intuitive explanations of the principles and tools for solving problems using modern analytic techniques and discusses their application to real problems in ways accessible and beneficial to practitioners across several areas--from science and engineering, to medicine, academia and commerce.

  • Includes input by practitioners for practitioners
  • Includes tutorials in numerous fields of study that provide step-by-step instruction on how to use supplied tools to build models
  • Contains practical advice from successful real-world implementations
  • Brings together, in a single resource, all the information a beginner needs to understand the tools and issues in data mining to build successful data mining solutions
  • Features clear, intuitive explanations of novel analytical tools and techniques, and their practical applications

Privacy in Statistical Databases: UNESCO Chair in Data Privacy, International Conference, Psd 2020, Tarragona, Spain, September 23-25, 2020, Proceedin

Автор: Domingo-Ferrer Josep, Muralidhar Krishnamurty
Название: Privacy in Statistical Databases: UNESCO Chair in Data Privacy, International Conference, Psd 2020, Tarragona, Spain, September 23-25, 2020, Proceedin
ISBN: 3030575209 ISBN-13(EAN): 9783030575205
Издательство: Springer
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Цена: 69870.00 T
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Описание: This book constitutes the refereed proceedings of the International Conference on Privacy in Statistical Databases, PSD 2020, held in Tarragona, Spain, in September 2020 under the sponsorship of the UNESCO Chair in Data Privacy. The 25 revised full papers presented were carefully reviewed and selected from 49 submissions.

Privacy in Statistical Databases

Автор: Domingo-Ferrer
Название: Privacy in Statistical Databases
ISBN: 3319453807 ISBN-13(EAN): 9783319453804
Издательство: Springer
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Цена: 46590.00 T
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Описание: This book constitutes the refereed proceedings of the International Conference on Privacy in Statistical Databases, PSD 2016, held in Dubrovnik, Croatia in September 2016 under the sponsorship of the UNESCO chair in Data Privacy. The scope of the conference is on following topics: tabular data protection; microdata and big data masking;

Privacy in Statistical Databases

Автор: Josep Domingo-Ferrer; Francisco Montes
Название: Privacy in Statistical Databases
ISBN: 331999770X ISBN-13(EAN): 9783319997704
Издательство: Springer
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Цена: 60550.00 T
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Описание: This book constitutes the refereed proceedings of the International Conference on Privacy in Statistical Databases, PSD 2018, held in Valencia, Spain, in September 2018 under the sponsorship of the UNESCO Chair in Data Privacy. The 23 revised full papers presented were carefully reviewed and selected from 42 submissions. The papers are organized into the following topics: tabular data protection; synthetic data; microdata and big data masking; record linkage; and spatial and mobility data.Chapter 'SwapMob: Swapping Trajectories for Mobility Anonymization' is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

Privacy in Statistical Databases

Автор: Josep Domingo-Ferrer
Название: Privacy in Statistical Databases
ISBN: 3319112562 ISBN-13(EAN): 9783319112565
Издательство: Springer
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Цена: 52170.00 T
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Описание: This book constitutes the refereed proceedings of the International Conference on Privacy in Statistical Databases, PSD 2014, held in Ibiza, Spain in September 2014 under the sponsorship of the UNESCO chair in Data Privacy.

RapidMiner

Автор: Hofmann Markus
Название: RapidMiner
ISBN: 1482205491 ISBN-13(EAN): 9781482205497
Издательство: Taylor&Francis
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Цена: 84710.00 T
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Описание: Powerful, Flexible Tools for a Data-Driven WorldAs the data deluge continues in today’s world, the need to master data mining, predictive analytics, and business analytics has never been greater. These techniques and tools provide unprecedented insights into data, enabling better decision making and forecasting, and ultimately the solution of increasingly complex problems. Learn from the Creators of the RapidMiner Software Written by leaders in the data mining community, including the developers of the RapidMiner software, RapidMiner: Data Mining Use Cases and Business Analytics Applications provides an in-depth introduction to the application of data mining and business analytics techniques and tools in scientific research, medicine, industry, commerce, and diverse other sectors. It presents the most powerful and flexible open source software solutions: RapidMiner and RapidAnalytics. The software and their extensions can be freely downloaded at www.RapidMiner.com. Understand Each Stage of the Data Mining ProcessThe book and software tools cover all relevant steps of the data mining process, from data loading, transformation, integration, aggregation, and visualization to automated feature selection, automated parameter and process optimization, and integration with other tools, such as R packages or your IT infrastructure via web services. The book and software also extensively discuss the analysis of unstructured data, including text and image mining. Easily Implement Analytics Approaches Using RapidMiner and RapidAnalytics Each chapter describes an application, how to approach it with data mining methods, and how to implement it with RapidMiner and RapidAnalytics. These application-oriented chapters give you not only the necessary analytics to solve problems and tasks, but also reproducible, step-by-step descriptions of using RapidMiner and RapidAnalytics. The case studies serve as blueprints for your own data mining applications, enabling you to effectively solve similar problems.

Data Mining with Decision Trees

Автор: Lior Rokach
Название: Data Mining with Decision Trees
ISBN: 981459007X ISBN-13(EAN): 9789814590075
Издательство: World Scientific Publishing
Цена: 110880.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:

Decision trees have become one of the most powerful and popular approaches in knowledge discovery and data mining; it is the science of exploring large and complex bodies of data in order to discover useful patterns. Decision tree learning continues to evolve over time. Existing methods are constantly being improved and new methods introduced.

This 2nd Edition is dedicated entirely to the field of decision trees in data mining; to cover all aspects of this important technique, as well as improved or new methods and techniques developed after the publication of our first edition. In this new edition, all chapters have been revised and new topics brought in. New topics include Cost-Sensitive Active Learning, Learning with Uncertain and Imbalanced Data, Using Decision Trees beyond Classification Tasks, Privacy Preserving Decision Tree Learning, Lessons Learned from Comparative Studies, and Learning Decision Trees for Big Data. A walk-through guide to existing open-source data mining software is also included in this edition.

This book invites readers to explore the many benefits in data mining that decision trees offer:

  • Self-explanatory and easy to follow when compacted
  • Able to handle a variety of input data: nominal, numeric and textual
  • Scales well to big data
  • Able to process datasets that may have errors or missing values
  • High predictive performance for a relatively small computational effort
  • Available in many open source data mining packages over a variety of platforms
  • Useful for various tasks, such as classification, regression, clustering and feature selection

Data Mining

Автор: Ye
Название: Data Mining
ISBN: 1138073660 ISBN-13(EAN): 9781138073661
Издательство: Taylor&Francis
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Цена: 64300.00 T
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Описание:

New technologies have enabled us to collect massive amounts of data in many fields. However, our pace of discovering useful information and knowledge from these data falls far behind our pace of collecting the data. Data Mining: Theories, Algorithms, and Examples introduces and explains a comprehensive set of data mining algorithms from various data mining fields. The book reviews theoretical rationales and procedural details of data mining algorithms, including those commonly found in the literature and those presenting considerable difficulty, using small data examples to explain and walk through the algorithms.

The book covers a wide range of data mining algorithms, including those commonly found in data mining literature and those not fully covered in most of existing literature due to their considerable difficulty. The book presents a list of software packages that support the data mining algorithms, applications of the data mining algorithms with references, and exercises, along with the solutions manual and PowerPoint slides of lectures.

The author takes a practical approach to data mining algorithms so that the data patterns produced can be fully interpreted. This approach enables students to understand theoretical and operational aspects of data mining algorithms and to manually execute the algorithms for a thorough understanding of the data patterns produced by them.


Statistical Methods for Recommender Systems

Автор: Agarwal
Название: Statistical Methods for Recommender Systems
ISBN: 1107036070 ISBN-13(EAN): 9781107036079
Издательство: Cambridge Academ
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Цена: 50680.00 T
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Описание: Designing algorithms to recommend items such as news articles and movies to users is a challenging task in numerous web applications. The crux of the problem is to rank items based on users' responses to different items to optimize for multiple objectives. Major technical challenges are high dimensional prediction with sparse data and constructing high dimensional sequential designs to collect data for user modeling and system design. This comprehensive treatment of the statistical issues that arise in recommender systems includes detailed, in-depth discussions of current state-of-the-art methods such as adaptive sequential designs (multi-armed bandit methods), bilinear random-effects models (matrix factorization) and scalable model fitting using modern computing paradigms like MapReduce. The authors draw upon their vast experience working with such large-scale systems at Yahoo! and LinkedIn, and bridge the gap between theory and practice by illustrating complex concepts with examples from applications they are directly involved with.


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