Statistics

Learning with uncertainty covers a broad range of scenarios in machine learning, this book mainly focuses on: (1) Decision tree learning with uncertainty, (2) Clustering under uncertainty environment, (3) Active learning based on uncertainty criterion,...

Feature engineering plays a vital role in big data analytics. Machine learning and data mining algorithms cannot work without data. Little can be achieved if there are few features to represent the underlying data objects, and the quality of results of...

Cyberspace is changing the face of crime. For criminals it has become a place for rich collaboration and learning, not just within one country; and a place where new kinds of crimes can be carried out, and a vehicle for committing conventional crimes...

Exploratory Data Analysis Using R provides a classroom-tested introduction to exploratory data analysis (EDA) and introduces the range of "interesting" – good, bad, and ugly – features that can be found in data, and why it is important to find them. It...

Event mining encompasses techniques for automatically and efficiently extracting valuable knowledge from historical event/log data. The field, therefore, plays an important role in data-driven system management. Event Mining: Algorithms and Applications...

Demonstrates how unsupervised learning approaches can be used for dimensionality reduction Neatly explains algorithms with focus on the fundamentals and underlying mathematical concepts Describes the comparative study of the algorithms and discusses when...

Since ROC curves have become ubiquitous in many application areas, the various advances have been scattered across disparate articles and texts. ROC Curves for Continuous Data is the first book solely devoted to the subject, bringing together all the...

Urban Informatics: Using Big Data to Understand and Serve Communities introduces the reader to the tools of data management, analysis, and manipulation using R statistical software. Designed for undergraduate and above level courses, this book is an...

The increasing complexity of insurance and reinsurance products has seen a growing interest amongst actuaries in the modeling of dependent risks. For efficient risk management, actuaries need to be able to answer fundamental questions such as: Is the...

Introduction to Machine Learning with Applications in Information Security, Second Edition provides a classroom-tested introduction to a wide variety of machine learning and deep learning algorithms and techniques, reinforced via realistic applications...

This book aims to teach core concepts of machine learning while focuses on modern applications. It is aimed at those who want to master machine learning by providing technical and practical insights. It avoids using complicated mathematics to explain how...

Apologies but we at Booksplease don't have a full description for this book.Book InformationISBN 9780730312932Author Ken BlackFormat PaperbackPage Count 640Imprint John Wiley & Sons Australia LtdPublisher John Wiley & Sons Australia...

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. ...

'Machine Learning for Knowledge Discovery with R' contains methodologies and examples for statistical modelling, inference, and prediction of data analysis. It includes most recent supervised and unsupervised machine learning methodologies such as...

Public Policy Analytics: Code & Context for Data Science in Government teaches readers how to address complex public policy problems with data and analytics using reproducible methods in R. Each of the eight chapters provides a detailed case study,...

Expertise in massive scale graph analytics is key for solving real-world grand challenges from health to sustainability to detecting insider threats, cyber defense, and more. Massive Graph Analytics provides a comprehensive introduction to massive graph...

Apologies but we at Booksplease don't have a full description for this book.Book InformationISBN 9780133022469Author David F. GroebnerFormat PaperbackPage Count 464Imprint PearsonPublisher Pearson Education (US)Weight(grams) 1102g

Smart Data: State-of-the-Art Perspectives in Computing and Applications explores smart data computing techniques to provide intelligent decision making and prediction services support for business, science, and engineering. It also examines the latest...

Statistical Learning using Neural Networks: A Guide for Statisticians and Data Scientists with Python introduces artificial neural networks starting from the basics and increasingly demanding more effort from readers, who can learn the theory and its...

1) Extensive compilation of commonly used statistical methods from fundamental to advancedlevel, which are essential for applied data scientists and practitioners in data science2) Straightforward explanations of the collected statistical theory and...

Learn how statistical information impacts decisions in today s business world as Camm/Cochran/Fry/Ohlmann/Anderson/Sweeney/Williams' leading ESSENTIALS OF STATISTICS FOR BUSINESS AND ECONOMICS, 10E connects key concepts in each chapter to actual business...

R for College Mathematics and Statistics encourages the use of R in mathematics and statistics courses. Instructors are no longer limited to ``nice'' functions in calculus classes. They can require reports and homework with graphs. They can do...

Advances in graph-based natural language processing (NLP) and information retrieval tasks have shown the importance of processing using the Graph of Words method. This book covers recent concrete information, from the basics to advanced level, about...

In the modern world, data is a vital asset for any organization, regardless of industry or size. The world is built upon data. However, data without knowledge is useless. The aim of this book, briefly, is to introduce new approaches that can be used to...

Text Mining and Visualization: Case Studies Using Open-Source Tools provides an introduction to text mining using some of the most popular and powerful open-source tools: KNIME, RapidMiner, Weka, R, and Python. The contributors—all highly experienced...

Analyzes the newly available statistical evidence on income distribution in the former Soviet Union both by social group and by republic, and considers the significance of inequalities as a factor contributing to the demise of the Communist regime.Book...

Probability as an Alternative to Boolean LogicWhile logic is the mathematical foundation of rational reasoning and the fundamental principle of computing, it is restricted to problems where information is both complete and certain. However, many...

As sensors become ubiquitous, a set of broad requirements is beginning to emerge across high-priority applications including disaster preparedness and management, adaptability to climate change, national or homeland security, and the management of...

Exploratory Data Analysis Using R provides a classroom-tested introduction to exploratory data analysis (EDA) and introduces the range of "interesting" - good, bad, and ugly - features that can be found in data, and why it is important to find them. It...

Sparse models are particularly useful in scientific applications, such as biomarker discovery in genetic or neuroimaging data, where the interpretability of a predictive model is essential. Sparsity can also dramatically improve the cost efficiency of...

Similar to other data mining and machine learning tasks, multi-label learning suffers from dimensionality. An effective way to mitigate this problem is through dimensionality reduction, which extracts a small number of features by removing irrelevant,...

This book covers techniques that can be used to analyze data from IoT sensors and addresses questions regarding the performance of an IoT system. It strikes a balance between practice and theory so one can learn how to apply these tools in practice with...

Utility-Based Learning from Data provides a pedagogical, self-contained discussion of probability estimation methods via a coherent approach from the viewpoint of a decision maker who acts in an uncertain environment. This approach is motivated by the...

Handbook of Big Data provides a state-of-the-art overview of the analysis of large-scale datasets. Featuring contributions from well-known experts in statistics and computer science, this handbook presents a carefully curated collection of techniques...

This textbook gives an overview of statistical methods that have been developed during the last years due to increasing computer use, including random number generators, Monte Carlo methods, Markov Chain Monte Carlo (MCMC) methods, Bootstrap, EM...

Overall, this is an appealing work for students and professionals, and is certain to remain as one of the key works in natural resource analysis. Mathematical Reviews Biological renewable resources, essential to the survival of mankind, are increasingly...

Unstructured Mining Approaches to Solve Complex Scientific Problems As the volume of scientific data and literature increases exponentially, scientists need more powerful tools and methods to process and synthesize information and to formulate new...

The book Bayesian Reasoning and Gaussian Processes for Machine Learning Applications talks about Bayesian Reasoning and Gaussian Processes in machine learning applications. Bayesian methods are applied in many areas such as game development, decision...

Drawing on the authors' two decades of experience in applied modeling and data mining, Foundations of Predictive Analytics presents the fundamental background required for analyzing data and building models for many practical applications, such as...

Event mining encompasses techniques for automatically and efficiently extracting valuable knowledge from historical event/log data. The field, therefore, plays an important role in data-driven system management. Event Mining: Algorithms and Applications...