Cambridge: Cambridge University Press, 2022. — 207 p. Data assimilation is a hugely important mathematical technique, relevant in fields as diverse as geophysics, data science, and neuroscience. This modern book provides an authoritative treatment of the field as it relates to several scientific disciplines, with a particular emphasis on recent developments from machine...
New York: Springer, 2017. - 386 p. This volume conveys some of the surprises, puzzles and success stories in high-dimensional and complex data analysis and related fields. Its peer-reviewed contributions showcase recent advances in variable selection, estimation and prediction strategies for a host of useful models, as well as essential new developments in the field. The continued...
Springer, 2023. — 409 p. This book presents some post-estimation and predictions strategies for the host of useful statistical models with applications in Data Science. It combines statistical learning and Machine Learning techniques in a unique and optimal way. It is well-known that Machine Learning methods are subject to many issues relating to bias, and consequently the mean...
Cambridge University Press, 1980. — 281 p. Prediction by its derivation means literally the stating beforehand of what will happen at some future time. It is an occupational hazard of many professions: meteorologist, doctor, economist, market researcher, engineering designer, politician and pollster. It is indeed a precarious game because any specific prediction can eventually...
CRC Press, 2021. — 272 p. — (Systems Innovation Book) — ISBN: 978-0-367-53741-8. Good data analytics is the basis for effective decisions. Whoever has the data, has the ability to extract information promptly and effectively to make pertinent decisions. The premise of this handbook is to empower users and tool developers with the appropriate collection of formulas and...
Wiley, 2023. — 546 p. — ISBN 9781119933243, 1119933242. Learn to gather and analyze publicly available data for your intelligence needs. In « Deep Dive: Exploring the Real-world Value of Open Source Intelligence » , veteran open-source intelligence analyst Rae Baker explains how to use publicly available data to advance your investigative OSINT skills and how your adversaries...
New York: Nova Science Pub Inc, 2021. — 190 p. When information in the data warehouse is processed, it follows a definite pattern. An unexpected deviation in the data pattern from the usual behavior is called an anomaly. The anomaly in the data is also referred to as noise, outlier, spammer, deviations, novelties and exceptions. Identification of the rare items, events,...
Boca Raton: CRC, 2017. — 582 p. Modern Data Science with R is a comprehensive data science textbook for undergraduates that incorporates statistical and computational thinking to solve real-world problems with data. Rather than focus exclusively on case studies or programming syntax, this book illustrates how statistical programming in the state-of-the-art R/RStudio computing...
Cambridge University Press, 2021. — 742 p. — ISBN 978-1-108-48301-8. This textbook provides future data analysts with the tools, methods, and skills needed to answer data-focused, real-life questions; to carry out data analysis; and to visualize and interpret results to support better decisions in business, economics, and public policy. Data wrangling and exploration,...
Society for Industrial and Applied Mathematics, 2001, -200 pp. As digital libraries and the World Wide Web (WWW) continue to grow exponentially, the ability to find useful information will greatly depend on the associated underlying framework of the indexing infrastructure or search engine. The push to get information on-line must be mediated by the design of automated...
2nd revised and extended Edition. — Springer, 2007. — 515 p. ISBN: 3540430601, 978-3540430605. This second and revised edition contains a detailed introduction to the key classes of intelligent data analysis methods. The twelve coherently written chapters by leading experts provide complete coverage of the core issues. The first half of the book is devoted to the discussion of...
This book contains simple examples to provide illustration. The data used in the examples contains information that is not based on any real companies or people. The author and publisher of this book have used their best efforts in preparing this book. The author and publisher make no warranty of any kind, expressed or implied, with regard to the documentation and examples...
Springer/Publishing House of Electronics Industry, 2023. — 353 p. Rather than presenting Python as Java or C, this textbook focuses on the essential Python programming skills for data scientists and advanced methods for big data analysts. Unlike conventional textbooks, it is based on Markdown and uses full-color printing and a code-centric approach to highlight the 3C...
Technical Report, Cambridge University, 2010. — 180 p. Computer programs to find formal proofs of theorems have a history going back nearly half a century. Originally designed as tools for mathematicians, modern applications of automated theorem provers and proof assistants are much more diverse. In particular they are used in formal methods to verify software and hardware...
The MIT Press, 2010. — 632 p. This text offers an introduction to the core topics underlying modern search technologies, including algorithms, data structures, indexing, retrieval, and evaluation. The emphasis is on implementation and experimentation; each chapter includes exercises and suggestions for student projects.
CRC Press, 2020. — 308 p. — ISBN: 978-0-367-35968-3. It’s All Analytics! The Foundations of AI, Big Data and Data Science Landscape for Professionals in Healthcare, Business, and Government (978-0-367-35968-3, 325690) Professionals are challenged each day by a changing landscape of technology and terminology. In recent history, especially in the last 25 years, there has been an...
John Wiley & Sons, Inc., 2017. — 416 p. — ISBN: 9781119092933. A comprehensive overview of data science covering the analytics, programming, and business skills necessary to master the discipline. Finding a good data scientist has been likened to hunting for a unicorn: the required combination of technical skills is simply very hard to find in one person. In addition, good data...
Wiley, 2004. — 220 p. — ISBN: 0470850558, 9780470850558, 9780470011287 +OCR With the advent of the Web along with the unprecedented amount of information available in electronic format, conceptual data analysis is more useful and practical than ever, because this technology addresses important limitations of the systems that currently support users in their quest for information....
Oxford: Oxford University Press, 2000. — 217 p. This book introduces the growing range of computational methods which arise from research into Artificial Intelligence (AI), and which are coming to be recognised as tools of great potential in modern scientific analysis. These methods offer tremendous potential in the analysis of scientific data: papers reporting the application of...
Springer, 2008. — 936 p. — ISBN: 978-3-540-33036-3. Complete overview of data visualization Comprehensive coverage of the field, both in theory and practice Visualizing the data is an essential part of any data analysis. Modern computing developments have led to big improvements in graphic capabilities and there are many new possibilities for data displays. This new volume in...
N.-Y.: Wiley, 2015(!). - 792p.
Data Mining Algorithms is a practical, technically-oriented guide to data mining algorithms that covers the most important algorithms for building classification, regression, and clustering models, as well as techniques used for attribute selection and transformation, model quality evaluation, and creating model ensembles. The author presents many...
Boca Raton: CRC Precc, 2021. — 196 p. "In this era of deep learning, where is our deeper understanding of AI? The answer is, here, in this book. Compton and Kang's ideas are a ‘must-read’ for anyone working with AI. Based on examples of real-world applications, they show us a better way to use AI. If your AI models are confusing to understand and hard to maintain, then this...
Boca Raton: CRC Press, 2014. — 300 p. — ISBN: 978-1-4822-1587-8. This book presents a philosophical study of statistics via the concept of data approximation. Developed by the well-regarded author, this approach discusses how analysis must take into account that models are, at best, an approximation of real data. It is, therefore, closely related to robust statistics and...
De Gruyter, 2022. — 490 p. — ISBN 978-3-11-069780-3. The amount of new information is constantly increasing, faster than our ability to fully interpret and utilize it to improve human experiences. Addressing this asymmetry requires novel and revolutionary scientific methods and effective human and Artificial Intelligence interfaces. By lifting the concept of time from a...
Springer, 1996. — 220 p. The concept of ridges has appeared numerous times in the image processing literature. Sometimes the term is used in an intuitive sense. Other times a concrete definition is provided. In almost all cases the concept is used for very specific applications. When analyzing images or data sets, it is very natural for a scientist to measure critical behavior...
Springer, 2023. — This book describes the probability theory associated with frequently used statistical procedures and the relation between probability theory and statistical inference. The first third of the book is dedicated to probability theory including topics relating to events, random variables, and the Central Limit Theorem. Statistical topics then include parameter...
O’Reilly Media, Inc., 2022. — 89 p. This book is an introduction to Causal Inference in Python, but it is not an introductory book in general. It’s introductory because I’ll focus on application, rather than rigorous proofs and theorems of causal inference; additionally, when forced to choose, I’ll opt for a simpler and intuitive explanation, rather than a complete and complex...
Amsterdam: Elsevier, 1994. — 365 p. — ISBN: 0-444-816593. Organizing knowledge is another way to contribute to its development. The value of such an attempt is in its capability for training, education and providing deepening insights. Separating the organization from the production of knowledge is arbitrary. Both are essential to the advancement of a field. How many times have...
Apple Academic Press/CRC Press, 2022. — 272 p. — ISBN 978-1771889681. In the current competitive and technology-driven world, Artificial Intelligence (AI) has emerged as growing technology that is applicable to many fields. As transactions and other business functions move online and grow more popular every year, the finance and banking industries face increasingly complex data...
2d ed.— London.: Springer-Verlag, 2006. — 256 p. — ISBN 9781852339760, 1852339764. This pioneering book deals specifically with the visualization of the Second-Generation Web. Now in its second edition it has been completely revised and updated, and includes extensive new material. It focuses on key topics including: • Visualization of semantic and structural information and...
New York: Apress, 2021. — 239 p. From deepfakes to GPT-3, deep learning is now powering a new assault on our ability to tell what’s real and what’s not, bringing a whole new algorithmic side to fake news. On the other hand, remarkable methods are being developed to help automate fact-checking and the detection of fake news and doctored media. Success in the modern business...
CRC Press, 2018. — 248 p. This is the age of data. There are more innovations and more opportunities for interesting work with data than ever before, but there is also an overwhelming amount of quantitative information being published every day. Data visualisation has become big business, because communication is the difference between success and failure, no matter how clever the...
Mercury Learning & Information, 2020. — 220 p. — ISBN: 978-1-68392-583-5. In the world of data science there are myriad tools available to analyze data. This book describes some of the popular software application tools along with the processes for downloading and using them in the most optimum fashion. The content includes data analysis using Microsoft Excel, KNIME, R, and...
Deepak Gupta, Siddhartha Bhattacharyya, Ashish Khanna, Kalpna Sagar. — JohnWiley&SonsLtd, 2020. — 432 p. — (Intelligent Signal and Data Processing). — ISBN: 978-1-119-54446-3. This book focuses on methods and tools for intelligent data analysis, aimed at narrowing the increasing gap between data gathering and data comprehension, and emphasis will also be given to solving of...
Academic Press, 2001. — 550 р. This book explores the concepts and techniques of data mining, a promising and ourishing frontier in database systems and new database applications. Data mining, also popularly referred to as knowledge discovery in databases (KDD), is the automated or convenient extraction of patterns representing knowledge implicitly stored in large databases,...
MIT Connection Science & Engineering, 2019. — 386 p. — ISBN-13: 978-0262043212. How to create an Internet of Trusted Data in which insights from data can be extracted without collecting, holding, or revealing the underlying data. Trusted Data describes a data architecture that places humans and their societal values at the center of the discussion. By involving people from all...
MIT Press, 2001. — 371 p. Machine learning has witnessed a resurgence of interest over the last few years, which is a consequence of the rapid development of the information industry. Data is no longer a scarce resource—it is abundant. Methods for "intelligent" data analysis to extract relevant information are needed. The goal of this book is to give a self-contained overview...
Springer, 2002. — 218 p. Text classification, or the task of automatically assigning semantic categories to natural language text, has become one of the key methods for organizing online information. Since hand-coding such classification rules is costly or even impractical, most modem approaches employ machine learning techniques to automatically learn text classifiers from...
Издательство Morgan Kaufmann, 1997, -594 pp. Following their genesis in library-based systems for searching bibliographic records, information retrieval, or IR, systems are now used for searching a vast range of text databases. This Reader provides a comprehensive introduction to the research that has been carried out to develop and to test the models and the techniques that...
Springer, 1998. — 623 p. Graphical models are a marriage between probability theory and graph theory. They provide a natural tool for dealing with two problems that occur throughout applied mathematics and engineering – uncertainty and complexity – and in particular they are playing an increasingly important role in the design and analysis of machine learning algorithms....
Packt Publishing, 2016. — 252 p. — ISBN: 978-1-78588-295-1. Design efficient machine learning systems that give you more accurate results. Machine learning is one of the fastest growing trends in modern computing. It has applications in a wide range of fields, including economics, the natural sciences, web development, and business modeling. In order to harness the power of...
CRC Press, 2022. — 278 p. — ISBN 978-1-032-11203-9. This book concentrates on mining networks, a subfield within data science. Data science uses scientific and computational tools to extract valuable knowledge from large data sets. Once data is processed and cleaned, it is analyzed and presented to support decision-making processes. Data science and machine learning tools have...
MIT Press, 1994. — 216 p. In the Fall term of 1990, we jointly taught a graduate seminar in computational learning theory in the computer science department of the University of California at Berkeley. The material that is presented here has its origins in that course, both in content and exposition. Rather than attempt to give an exhaustive overview of this rapidly expanding...
Wiley, 2019. — 120 р. — ISBN: 1119570700. The essential guide for data scientists and for leaders who must get more from their data science teams. It is no secret that “data,” broadly defined, is all the rage. And “data science,” including traditional statistics, Bayesian statistics, business intelligence, predictive analytics, Big Data, Machine Learning (ML), and Artificial...
N.-Y.: Springer, 2005. — 268 p. — — ISBN: 354028348X, 9783540283485.
Clustering is one of the most fundamental and essential data analysis techniques. Clustering can be used as an independent data mining task to discern intrinsic characteristics of data, or as a preprocessing step with the clustering results then used for classification, correlation analysis, or anomaly...
Germany, Springer, 2001 - 501 стр
Mathematical Preliminaries
Neural Modeling
The Basic SOM
Physiological Interpretation of SOM
Variants of SOM
Learning Vector Quantization
Application
Software Tools for SOM
Hardware for SOM
An Overview of SOM Literature
Springer, 2005. — 713 p. The book takes a modest attempt to cover the entire framework of computational intelligence and its applications in a single volume. It includes 23 chapters, covering all aspects of the subject in a clear, precise and highly comprehensive style. The book also includes two appendices. Appendix-A contains sample runs of programs, the source codes of which...
Horwood Publishing Limited, 2007. — 475 p. — ISBN: 1904275214, 978-1904275213. Data mining is often referred to by real-time users and software solutions providers as knowledge discovery in databases (KDD). Good data mining practice for business intelligence (the art of turning raw software into meaningful information) is demonstrated by the many new techniques and developments...
CRC Press, 2022. — 346 p. — (AK Peters Visualization Series). — ISBN: 978-0-367-54842-1. Mobile Data Visualization is about facilitating access to and understanding of data on mobile devices. Wearable trackers, mobile phones, and tablets are used by millions of people each day to read weather maps, financial charts, or personal health meters. What is required to create...
Academic Press, Elsevier, 2022. — 598 p. — ISBN: 978-0-12-824447-0. Tensors for Data Processing: Theory, Methods and Applications presents both classical and state-of-the-art methods on tensor computation for data processing, covering computation theories, processing methods, computing and engineering applications, with an emphasis on techniques for data processing. This...
MIT Press, 2019. — 272 p. — ISBN: 978-0262039666. How to analyze data settings rather than data sets, acknowledging the meaning-making power of the local. In our data-driven society, it is too easy to assume the transparency of data. Instead, Yanni Loukissas argues in All Data Are Local, we should approach data sets with an awareness that data are created by humans and their...
Boca Raton: CRC Press, 2024. — 243 p. The text discusses the techniques of Deep Learning and Machine Learning in the field of neuroscience, engineering approaches to study the brain structure and dynamics, convolutional networks for fast, energy-efficient neuromorphic computing, and reinforcement learning in feedback control. It showcases case studies in neural data analysis....
Manning, 2016. — 325 p. — ISBN: 978-1617292583. There’s priceless insight trapped in the flood of data users leave behind as they interact with web pages and applications. Those insights can be unlocked by using intelligent algorithms like the ones that have earned Facebook, Google, Twitter, and Microsoft a place among the giants of web data pattern extraction. Improved search,...
Boca Raton: CRC Press, Taylor & Francis Group, LLC, 2022. — 437 p. Object Oriented Data Analysis is a framework that facilitates inter-disciplinary research through new terminology for discussing the often many possible approaches to the analysis of complex data. Such data are naturally arising in a wide variety of areas. This book aims to provide ways of thinking that enable...
Morgan Kaufmann, 2007. — 496 p. — ISBN: 978-0123704801, 0123704804. 3rd Edition. Traditionally, networking has had little or no basis in analysis or architectural development, with designers relying on technologies they are most familiar with or being influenced by vendors or consultants. However, the landscape of networking has changed so that network services have now become...
Introduction to Data Mining Soft Computing Multimedia Data Compression String Matching Classification in Data Mining Clastering in Data Mining Association Rules Rule Mining with Soft Computing Multimedia Data Mining Bioinformatics: An Application
Books on Demand GmbH, 2000 — 252 p. — ISBN 9783898118613,3898118614 Today, there is an increased need to extract information for decision making from a large collection of data. This transformation of data into knowledge is an interactive and iterative process of various subtasks and decisions, and is called Knowledge Discovery from Data. The central part of Knowledge Discovery...
Springer, 2001. — 244 p. This text and software package introduces readers to automated theorem proving, while providing two approaches implemented as easy-to-use programs. These are semantic-tree theorem proving and resolution-refutation theorem proving. The early chapters introduce first-order predicate calculus, well-formed formulae, and their transformation to clauses. Then...
Burlington: Elsevier Inc., 2009. — 860 p. — ISBN 978-0-12-374765-5. Книга посвящена быстро развивающемуся разделу анализа данных - Data Mining, то есть поиску закономерностей в больших массивах данных. Включает в себя сводку теоретических принципов, основные алгоритмы, включая предназначенные для обработки данных на естественном языке, подробный предназначенный для...
New York: Springer, 2007. — 576 p.
This volume presents an extensive collection of contributions covering aspects of the exciting and important research field of data mining techniques in biomedicine. Coverage includes new approaches for the analysis of biomedical data; applications of data mining techniques to real-life problems in medical practice; comprehensive reviews of...
Society for Industrial and Applied Mathematics, 2005, -316 pp. Data mining may be defined broadly as the use of automated procedures to extract useful information and insight from large data sets. In practice, these data sets contain various types of anomalous records that significantly complicate the analysis problem. In particular, the prevalence of outliers, missing or...
Piegorsch Walter W., Levine Richard A., Zhang Hao Helen, Lee Thomas C.M. — John Wiley & Sons, 2022. — 674 p. — ISBN: 978-1119561071. An essential roadmap to the application of computational statistics in contemporary data science In Computational Statistics in Data Science, a team of distinguished mathematicians and statisticians delivers an expert compilation of concepts,...
Wiley-Scrivener, 2022. — 471 p. — (Next-Generation Computing and Communication Engineering). — ISBN 1119857333. This desk reference handbook gives a hands-on experience on various algorithms and popular techniques used in real-time in data science to all researchers working in various domains. Data Science is one of the leading research-driven areas in the modern era. It is...
Springer, 2023. — 251 p. Artificial Intelligence (AI) is now everywhere and fuels both industry and daily life all over the world. We are in the era of “Big Data,” and huge sums of information can be obtained which are too cumbersome for people to process themselves. These Big Data are even with much complex correlations behind them in various areas, such as computer vision and...
Arcler Press, 2020. — 278 p. — ISBN: 978-1-77361-370-3. Data Reduction and Analysis examines various aspects of data analysis including brief overview of data environment. It starts with establishing a clear picture of statistics to data science with Python and R. It comprise concepts of data analysis and error analysis. Provides the reader with insights into the reduction and...
MIT Press, 2005. — 260 p. The book is primarily intended for graduate students and researchers in machine learning at departments of Computer Science, Statistics and Applied Mathematics. As prerequisites we require a good basic grounding in calculus, linear algebra and probability theory as would be obtained by graduates in numerate disciplines such as electrical engineering,...
San Diego: Cognella Academic Publishing, 2019. — 355 p. Probability for Data Scientists provides students with a mathematically sound yet accessible introduction to the theory and applications of probability. Students learn how probability theory supports statistics, data science, and machine learning theory by enabling scientists to move beyond mere descriptions of data to...
New York: Columbia University Press, 2021. — 464 p. Now more than ever, content must be visual if it is to travel far. Readers everywhere are overwhelmed with a flow of data, news, and text. Visuals can cut through the noise and make it easier for readers to recognize and recall information. Yet many researchers were never taught how to present their work visually. This book...
John Wiley, 1999. — 349 p. The subject of stochastic dynamic programming, also known as stochastic optimal control, Markov decision processes, or Markov decision chains, encompasses a wide variety of interest areas and is an important part of the curriculum in operations research, management science, engineering, and applied mathematics departments. This book is unique in its...
The Institution of Engineering and Technology, 2022. — 327 р. — (Control, Robotics and Sensors). The energy efficiency paradigm associated with Wireless Sensor Networks (WSNs) and the Internet of Things (IoT) is a major bottleneck for the development of related technologies. To overcome this limitation, the design and development of efficient and high-performance energy...
Wiley-ISTE, 2019. — 286 p. — ISBN: 9781119597575. The data analysis field has been continuously growing over recent decades following the wide applications of computing and data collection along with new developments in analytical tools. Therefore, the need for publications is evident. New publications appear as printed or e-books covering the need for information from all...
New York: Springer, 2017. — 456 p. — (Texts in Computer Science). What is Data science. Mathematical Preliminaries. Data Munging. Scores and Rankings. Statistical Analysis. Visualizing Data. Mathematical Model. Linear Algebra. Linear and Logistic Regression. Distance and Network Methods. Machine Learning. Big Data: Achieving Scale. Coda.
Oxford University Press, 2020. — 272 p. — ISBN: 978–0–19–884439–6. Data science has never had more influence on the world. Large companies are now seeing the benefit of employing data scientists to interpret the vast amounts of data that now exists. However, the field is so new and is evolving so rapidly that the analysis produced can be haphazard at best. The 9 Pitfalls of...
Chennai: MJP Publishers, 2019. — 383 p. If you are looking for a complete step-by-step instructions for learning R Programming for Statistical Data Analysis, Graphical Visualization and Data Mining, authors Dr. Sudhamathy & Dr. Jothi Venkateswaran’s “R Programming - An Approach to Data Analytics” is a hands-on book packed with examples and references that would help you get...
Издательство IOS Press, 2003, -433 pp. In recent years, considerable progress has been made in the understanding of problems of learning and generalization. In this context, intelligence basically means the ability to perform well on new data after learning a model on the basis of given data. Such problems arise in many different areas and are becoming increasingly important...
Wiley-Scrivener, 2024. — 372 p. Explore the cutting-edge realms of data engineering in multimedia with Supervised and Unsupervised Data Engineering for Multimedia Data, where expert contributors delve into innovative methodologies, offering invaluable insights to empower both novices and seasoned professionals in mastering the art of manipulating multimedia data with precision...
Second Edition. — Chapman and Hall/CRC, 2004. — 296 p. — ISBN: 1584883855. Since the first edition of this book appeared, computers have come to the aid of modern experimenters and data analysts, bringing with them data analysis techniques that were once beyond the calculational reach of even professional statisticians. Today, scientists in every field have access to the...
CRC Press, 2023. — 253 р. — ISBN: 978-1-032-03570-3. Hybrid Intelligent Systems for Information Retrieval covers three areas along with the introduction to Intelligent IR, i.e., Optimal Information Retrieval Using Evolutionary Approaches, Semantic Search for Web Information Retrieval, and Natural Language Processing for Information Retrieval. This book covers the architectures...
Boca Raton: CRC Press, 2022. — 443 p. Data Science: A First Introduction focuses on using the R programming language in Jupyter notebooks to perform data manipulation and cleaning, create effective visualizations, and extract insights from data using classification, regression, clustering, and inference. The text emphasizes workflows that are clear, reproducible, and shareable,...
Boca Raton: CRC Press, 2024. — 590 p. As data continues to grow exponentially, knowledge of Data Science and Machine Learning has become more crucial than ever. Machine Learning has grown exponentially; however, the abundance of resources can be overwhelming, making it challenging for new learners. This book aims to address this disparity and cater to learners from various...
New York: Chapman and Hall/CRC, 2021. — 483 p. Data science is a multi-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured (labelled) and unstructured (unlabelled) data. It is the future of Artificial Intelligence (AI) and the necessity of future to make things easier and more productive. In simple...
New York: Springer, 2021. — 271 p. This textbook grew out of notes for the ECE143 Programming for Data Analysis class that the author has been teaching at University of California, San Diego, which is a requirement for both graduate and undergraduate degrees in Machine Learning and Data Science. This book is ideal for readers with some Python programming experience. The book...
New York: Chapman and Hall/CRC, 2021. — 297 p. Fundamentals of Data Science is designed for students, academicians and practitioners with a complete walkthrough right from the foundational groundwork required to outlining all the concepts, techniques and tools required to understand Data Science.Data Science is an umbrella term for the non-traditional techniques and...
Springer, 2022. — 371 p. — (Undergraduate Topics in Computer Science). — ISBN 3030976440. This book is about the harmonious synthesis of functional programming and numerical computation . It shows how the expressiveness of OCaml allows for fast and safe development of data science applications . Step by step, the authors build up to use cases drawn from many areas of Data...
Springer, 2005. – 174 p. – ISBN: 0387242465, 9780387242477 The focus of Mining Sequential Patterns from Large Data Sets is on sequential pattern mining. In many applications, such as bioinformatics, web access traces, system utilization logs, etc., the data is naturally in the form of sequences. This information has been of great interest for analyzing the sequential data to...
The MIT Press, 2023. — 201 p. How the use of Machine Learning to analyze art images has revived formalism in art history, presenting a golden opportunity for art historians and computer scientists to learn from one another. Though formalism is an essential tool for art historians, much recent art history has focused on the social and political aspects of art. But now art...
John Wiley, 2001. — 288. Textbooks on fuzzy systems, system theory, time-series and data analysis traditionally describe a theoretical framework or particular methodology and then apply these concepts to problems. I believe that such a strategy is not optimal, nor docs it seems adequate to deal with the current challenges in science and engineering. Researchers in system and...
Lawrence Erlbaum, 2003. — 703 р. Created with the input of a distinguished International Board of the foremost authorities in data mining from academia and industry, The Handbook of Data Mining presents comprehensive coverage of data mining concepts and techniques. Algorithms, methodologies, management issues, and tools are all illustrated through engaging examples and...
Springer, 1999. — 349 p. Two volumes (Vol. 2 /file/1863167/, /file/1863172/) consisting of Foundations and Applications provide the current status of theoretical and empirical developments in "computing with words". In philosophy, the twentieth century is said to be the century of language. This is mainly due to Wittgenstein who said: "The meaning of a word is its use in the...
Springer, 1999. — 610 p. Two volumes (Vol. 1 /file/1863159/, /file/1863162/) consisting of Foundations and Applications provide the current status of theoretical and empirical developments in "computing with words". In philosophy, the twentieth century is said to be the century of language. This is mainly due to Wittgenstein who said: "The meaning of a word is its use in the...
Томск: Томский университет, 2014. — 442 с. — ISBN 978-5-7511-2235-5. Абрамова Т.В., Ваганова Е.В., Горбачев С.В., Сырямкин В.И., Сырямкин М.В. В монографии представлены нейро-нечеткие методы и алгоритмы интеллектуального анализа многомерных сложно построенных данных. Рассмотрены применения нейросетевых технологий в экономике, распознавании образов, навигации, управлении, поиске...
Барсегян А. А., Куприянов М. С., Степаненко В. В., Холод И. И. - Методы и модели анализа данных: OLAP и Data Mining. СПб.: БХВ-Петербург, 2004. - 336 с: ил. 331 с.
В книге освещены основные направления в области анализа данных: организация хранилища данных, оперативный (OLAP) и интеллектуальный (Data Mining) анализ данных. Приведено описание методов и алгоритмов решения...
384 Стр. | ISBN: 5941579918 | Издатель: БХВ-Петербург | Серия: Учебное пособие | 2007 Книга является вторым, обновленным и дополненным, изданием учебного пособия «Методы и модели анализа данных. OLAP и Data Mining». Излагаются основные направления в области разработки корпоративных систем: организация хранилищ данных, распределенный, оперативный (OLAP), интеллектуальный (Data...
СПб.: БХВ-Петербург, 2004. 336с.: ил
В книге освещены основные направления в области анализа данных: организация хранилища данных, оперативный (OLAP) и интеллектуальный (Data Mining) анализ данных. Приведено описание методов и алгоритмов решения основных задач: классификация, кластеризация и др. Описание идеи каждого метода дополняется конкретным примером его применения....
СПб.: БХВ-Петербург, 2009. — 512 с. — 3-е изд. — ISBN: 978-5-9775-0368-6. Излагаются основные направления в области разработки корпоративных систем: организация хранилищ данных, оперативный (OLAP) и интеллектуальный (Data Mining) анализ данных. В третьем издании по сравнению со вторым, выходившем под названием "Технологии анализа данных: Data Mining, Text Mining, Visual Mining,...
М.: Горячая линия-Телеком, 2013. — 288 с. — ISBN: 978-5-9912-0326-5. Учебное пособие для вузов открывает широкому кругу читателей современный анализ данных в программе Statistica. Statistica (производитель StatSoft, USA) занимает лидирующее положение среди программ анализа данных и имеет сотни тысяч зарегистрированных пользователей в России и мире. На простых, ясных примерах...
Санкт-Петербург: БХВ, 2018. — 304 с. — ISBN: 978-5-9775-3974-6. Книга рассчитана на специалистов в области Data Science, обладающих некоторым опытом работы с языком программирования R и имеющих предварительное понятие о математической статистике. В ней в удобной и легкодоступной форме представлены ключевые понятия из статистики, которые относятся к науке о данных, а также...
М.: Мир, 2007. — 187 с. Книга ориентирована не только на представителей специальных служб, но также на сотрудников информационно-аналитических подразделений предприятий и политических организаций, на журналистов, социологов, научных работников. Она может быть полезной для любого, кто из любопытства или с практической целью желает разобраться в технологиях аналитической работы...
Лабораторный практикум. — М.: МИСИС, 1982. — 132 с. Введение. Экстремальная группировка параметров. Автоматическая классификация объектов. Лингвистический анализ данных. Приложения.
Пер. с англ. — М.: Альпина, 2001. — 317 с. — ISBN 5-89684-013-6. В своей работе современные менеджеры сталкиваются со все возрастающим потоком информации, которую необходимо проанализировать, обобщить и на ее основании принять решение. С каждым годом это становится все сложнее и сложнее, так как объем доступной информации (во многом благодаря развитию Интернета) постоянно...
М.: Горячая Линия - Телеком, 2012. — 156 с. Рассмотрены методы кластеризации технического состояния зданий и сооружений в условиях неопределенности, основанные на комплексном использовании инструментария теории нечётких множеств и генетических алгоритмов, позволяющего устранить недостатки существующих аналогов, обеспечивая при этом высокую обоснованность и адекватность принимаемых...
СПб: Питер, 2001. — 368 с. — ISBN: 5-318-00227-7 Не новая, но прекрасная книга по Data Mining на русском. Содержит все основные методы, интересные примеры, понятные формулы, код. Впервые в электронном виде! В книге приводится объективный аналитический обзор методов и программных продуктов Data Mining. Подробно рассматриваются статистические пакеты, нейросети, эволюционные...
Новосибирск: ИМ СО РАН, 1999. — 270 c.
Введение в анализ данных
Основные понятия
Классификация задач анализа данных
Базовые гипотезы, лежащие в основе методов анализа данных
Методы анализа данных
Задача таксономии
Распознавание образов
Главная цель машинной обработки экспериментальных или статистических данных состоит в автоматическом обнаружении скрытых в них...
Новосибирск, Наука, 1979. - 120 с. Книга посвящена вопросам теории и приложений методов эмпирического предсказания и важному частному случаю этого научного направления - автоматическому распознаванию образов. Рассмотрено соотношение между проблемами эмпирического предсказания и искусственного интеллекта. Даны способы описания исходных данных, обнаружения закономерностей и их...
Учебное пособие. — Томск: Томский государственный университет, 2020. — 196 с. — ISBN 978-5-94621-898-6. В учебном пособии рассматриваются вопросы, связанные с популярной сегодня областью машинного обучения и интеллектуального анализа данных. Исследуются основные технологические тренды, наиболее активно использующие алгоритмы интеллектуальной обработки данных, – бизнес,...
Под ред. И. С. Енюкова. — М.: Финансы и статистика, 1989. — 215 с: ил. Книга представляет сборник работ американских учёных, в которых рассмотрен аппарат факторного, дискриминантного кластерного анализа, широко применяемый в социально-экономических классификациях и анализе неявных закономерностей в экономических и социальных процессах. Для научных работников, преподавателей,...
Пер. с англ. Слинкин А. А. — М.: ДМК Пресс, 2016. — 500 с.: ил. — ISBN: 978-5-97060-190-7. Анализ больших наборов данных — Эта книга написана ведущими специалистами в области технологий баз данных и веба. Благодаря популярности интернет-торговли появилось много чрезвычайно объемных баз данных, для извлечения информации из которых нужно применять методы добычи данных (data...
М.: Вильямс, 2011. — 512 с. — ISBN 978-5-8459-1623-5. Введение в информационный поиск - это первый учебник, который содержит взаимосвязанное изложение проблем классического информационного поиска и поиска в вебе, включая смежные задачи классификации и кластеризации текстов. Об авторах Кристофер Д. Маннинг - профессор информатики и лингвистики Станфордского университета....
Пер. с анл. — СПб.: Диалектика, 2020. — 528 с.: ил. — ISBN 978-5-907203-20-4. Введение в информационный поиск — это первый учебник, который содержит взаимосвязанное изложение проблем классического информационного поиска и поиска в вебе, включая смежные задачи классификации и кластеризации текстов. Учебник написан с позиций информатики (computerscience) и содержит современное...
Миркин Б.Г. Введение в анализ данных. Учебник и практикум для бакалавриата и магистратуры. -М.:Юрайт, 2015. - 174 с.
В учебнике, подготовленном на основе большого международного опыта исследований и преподавания, излагаются основные методы анализа данных. Изложение ориентировано на людей, предпочитающих не формулы, а вычисления.
СПб.: БХВ, 2021. — 240 с. Рассмотрены основные методы статистической обработки данных и машинного обучения и их реализация на языке R. Изложены принципы разработки информационных систем и моделей, основанных на данных. Приведены примеры использования методов машинного обучения при решении прикладных задач. Рассмотрены подготовка данных, работа с временными рядами, методы...
Красноярск: ИПЦ КГТУ, 2004. — 319 с.
Представлены основные идеи, методы и алгоритмы статистической проверки гипотез, классификации в распознавании образов, планирования эксперимента, непараметрической обработки информации, экспериментальной оптимизации, идентификации статических и динамических моделей объектов, адаптивного управления с идентификацией. Приведены методы обработки...
СПб.: Питер, 2017. — 336 с. — (Библиотека программиста). — ISBN: 9785496025171. Data Science - это совокупность понятий и методов, позволяющих придать смысл и понятный вид огромным объемам данных. Каждая из глав этой книги посвящена одному из самых интересных аспектов анализа и обработки данных. Вы начнете с теоретических основ, затем перейдете к алгоритмам машинного обучения,...
Київ: КНЕУ, 2007. — 376 c. — ISBN: 9789664830185 У виданні розглянуто питання створення і застосування систем інтелектуального аналізу даних — дейтамайнінгу. Стисло проаналізовано концепцію дейтамайнінгу, наведено приклади його застосування в економіці й бізнесі, описано найсучасніші пакети програмного забезпечення дейтамайнінгу, викладено питання архітектури, класифікації та...
Учебное пособие. — Самара: Самарский университет, 2019. — 176 с. — ISBN: 978-5-7883-1414-3. В данном учебном пособии содержится описание основных методов и алгоритмов интеллектуального анализа данных (Data Mining, Machine Learning). Разбираются примеры учебных практических задач, в том числе с использованием системы R статистического анализа данных и графики. Пособие может быть...
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