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...
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...
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; carry out data analysis; and visualize and interpret results to support better decisions in business, economics, and public policy. Data wrangling and exploration, regression...
Society for Industrial and Applied Mathematics, 2001, -200 p. 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...
Pierre Bessiere, Emmanuel Mazer, Juan Manuel Ahuactzin, Kamel Mekhnacha. — CRC Press, 2014. — 378 p. — (Machine Learning & Pattern Recognition) — ISBN13: 978-1-4398-8033-3. Probability as an Alternative to Boolean Logic While logic is the mathematical foundation of rational reasoning and the fundamental principle of computing, it is restricted to problems where information is...
119p. ISBN: 0-07-005779-6. 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...
Cambridge University Press, 2020. — 424 p. — ISBN: 978-1108755528. This book provides an introduction to the mathematical and algorithmic foundations of data science, including machine learning, high-dimensional geometry, and analysis of large networks. Topics include the counterintuitive nature of data in high dimensions, important linear algebraic techniques such as singular...
Editors: Mahdi Bohlouli, Bahram Sadeghi Bigham, Zahra Narimani, Mahdi Vasighi, Ebrahim Ansari. — Springer, 2020. — xi, 338 p. — (ecture Notes on Data Engineering and Communications Technologies 45). — ISBN: 978-3-030-37308-5. This book presents outstanding theoretical and practical findings in data science and associated interdisciplinary areas. Its main goal is to explore how...
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...
Bouveyron Charles, Celeux Gilles, Murphy T. Brendan, Raftery Adrian E. — Cambridge University Press, 2019. — 447 p. — (Statistical and Probabilistic Mathematics). — ISBN: 978-1-108-49420-5. Cluster analysis finds groups in data automatically. Most methods have been heuristic and leave open such central questions as: how many clusters are there? Which method should I use? How...
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...
Early Release. — O’Reilly Media, 2016. — 90 p. — ISBN13: 978-1-491-95289-4. A key component of data science is statistics and machine learning, but only a small proportion of data scientists are actually trained as statisticians. This concise guide illustrates how to apply statistical concepts essential to data science, with advice on how to avoid their misuse. Many courses and...
The MIT Press, 632 p., 2010. 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.
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...
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...
CRC Press, 2019. — 147 p. — ISBN13: 978-0-367-25439-1. This book emphasizes various image shape feature extraction methods which are necessary for image shape recognition and classification. Focussing on a shape feature extraction technique used in content-based image retrieval (CBIR), it explains different applications of image shape features in the field of content-based...
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(!). - 792 p. 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...
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...
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...
Dieter Fensel, Umutcan Şimşek, Kevin Angele, Elwin Huaman, Elias Kärle, Oleksandra Panasiuk, Ioan Toma, Jürgen Umbrich, Alexander Wahler. — Springer, 2020. — 156 p. — ISBN: 978-3-030-37438-9. This book describes methods and tools that empower information providers to build and maintain knowledge graphs, including those for manual, semi-automatic, and automatic construction;...
O’Reilly, 2018. — 168 p. — ISBN: 978-1-491-92846-2. You have a mound of data front of you and a suite of computation tools at your disposal. Which parts of the data actually matter? Where is the insight hiding? If you’re a data scientist trying to navigate the murky space between data and insight, this practical book shows you how to make sense of your data through high-level...
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...
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...
Apress, 2014. — 348 p. — ISBN: 978-1430265986. Data Scientists at Work is a collection of interviews with sixteen of the world's most influential and innovative data scientists from across the spectrum of this hot new profession. "Data scientist is the sexiest job in the 21st century," according to the Harvard Business Review. By 2018, the United States will experience a...
Academic Press, 2001. — 550 p. 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 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...
Morgan Kaufmann, 1997, -594 p. 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 can be exploited...
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....
3rd Edition. — Wiley-IEEE Press, 2020. — 638 p. — ISBN: 978-1119516040. Presents the latest techniques for analyzing and extracting information from large amounts of data in high-dimensional data spaces The revised and updated third edition of Data Mining contains in one volume an introduction to a systematic approach to the analysis of large data sets that integrates results...
De Gruyter, 2019. — 398 p. — ISBN: 978-3-11-049635-2. This graduate text covers a variety of mathematical and statistical tools for the analysis of big data coming from biology, medicine and economics. Neural networks, Markov chains, tools from statistical physics and wavelet analysis are used to develop efficient computational algorithms, which are then used for the processing...
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 p. — 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...
Packt Publishing, 2019. — 373 p. — ISBN: 978-1-83855-102-5. Gain hands-on experience with industry-standard data analysis and machine learning tools in Python Data Science Projects with Python is designed to give you practical guidance on industry-standard data analysis and machine learning tools in Python, with the help of realistic data. The book will help you understand how...
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...
Packt Publishing, 2018. — 414 p. — ISBN: 978-1-78913-940-2. A hands-on guide for professionals to perform various data science tasks in R R is the most widely used programming language, and when used in association with data science, this powerful combination will solve the complexities involved with unstructured datasets in the real world. This book covers the entire data...
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....
Packt Publishing, 2019. — 387 p. — ISBN: 978-1-78980-735-6. Take your first steps to become a fully qualified data analyst by learning how to explore large relational datasets. Understanding and finding patterns in data has become one of the most important ways to improve business decisions. If you know the basics of SQL, but don’t know how to use it to gain business insights...
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...
O’Reilly Media, 2016. — 65 p. — ISBN: 978-1-491-92840-0. You have a mound of data sitting in front of you and a suite of computation tools at your disposal. And yet, you're stumped as to how to turn that data into insight. Which part of that data actually matters, and where is this insight hidden? If you're a data scientist who struggles to navigate the murky space between data...
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
Cambridge University Press, 2019. — 561 p. — ISBN: 978-1-107-07615-0. Variational Bayesian learning is one of the most popular methods in machine learning. Designed for researchers and graduate students in machine learning, this book summarizes recent developments in the non-asymptotic and asymptotic theory of variational Bayesian learning and suggests how this theory can be...
2nd Edition. — Packt Publishing, 2018. — 207 p. — ISBN: 978-1-78980-607-6. Build a strong foundation of machine learning algorithms in 7 days Machine learning applications are highly automated and self-modifying, and continue to improve over time with minimal human intervention, as they learn from the trained data. To address the complex nature of various real-world data...
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...
O’Reilly Media, Inc., 2022. — 350 p. — ISBN: 978-1-098-10293-7. To succeed in data science you need some math proficiency. But not just any math. This common-sense guide provides a clear, plain English survey of the math you'll need in data science, including probability, statistics, hypothesis testing, linear algebra, machine learning, and calculus. Practical examples with...
O’Reilly Media, 2020. — 500 p. — ISBN: 978-1-492-04165-8. Time series data analysis is increasingly important due to the massive production of such data through the internet of things, the digitalization of healthcare, and the rise of smart cities. As continuous monitoring and data collection become more common, the need for competent time series analysis with both statistical...
Burlington: Elsevier Inc., 2009. — 860 p. — ISBN: 978-0-12-374765-5. The book is devoted to the rapidly developing section of data analysis - Data Mining, that is, the search for patterns in large data sets. Includes a summary of theoretical principles, basic algorithms, including those designed for natural language data processing, a detailed self-study case study of this...
Society for Industrial and Applied Mathematics, 2005, -316 p. 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...
River Publishers, 2019. — 236 p. — ISBN: 978-87-93609-69-3. Nowadays, the prevalence of computing systems in our lives is so ubiquitous that we live in a cyber-physical world dominated by computer systems, from pacemakers to cars and airplanes. These systems demand more computational performance to process large amounts of data from multiple data sources with guaranteed...
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 many complex correlations behind them in various areas, such as computer vision 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,...
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...
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. 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
Anthony So, Thomas V. Joseph, Robert Thas John, Andrew Worsley, Dr. Samuel Asare. — Packt Publishing, 2020. — 816 p. — ISBN: 978-1-83898-126-6. Cut through the noise and get real results with a step-by-step approach to data science You already know you want to learn data science, and a smarter way to learn data science is to learn by doing. The Data Science Workshop focuses on...
Springer, 2020. — 317 p. — ISBN: 978-981-15-2444-8. This book discusses topics related to bioinformatics, statistics, and Machine Learning, presenting the latest research in various areas of bioinformatics. It also highlights the role of computing and Machine Learning in knowledge extraction from biological data, and how this knowledge can be applied in fields such as drug...
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...
IOS Press, 2003, -433 p. 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 and crucial...
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...
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 the 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...
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 the 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...
Springer, 1999. — 349 p. Two volumes 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 language game". "The concept game is a...
Springer, 1999. — 610 p. Two volumes (Vol. 1,) 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 language game". "The concept...
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