A comprehensive introduction to the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications.
Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context.
After discussing the trajectory from data to insight to decision, the book describes four approaches to machine learning: information-based learning, similarity-based learning, probability-based learning, and error-based learning. Each of these approaches is introduced by a nontechnical explanation of the underlying concept, followed by mathematical models and algorithms illustrated by detailed worked examples. Finally, the book considers techniques for evaluating prediction models and offers two case studies that describe specific data analytics projects through each phase of development, from formulating the business problem to implementation of the analytics solution. The book, informed by the authors' many years of teaching machine learning, and working on predictive data analytics projects, is suitable for use by undergraduates in computer science, engineering, mathematics, or statistics; by graduate students in disciplines with applications for predictive data analytics; and as a reference for professionals.
Описание: Like the popular second edition, Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. Inside, you'll learn all you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining?including both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. <br><br>Complementing the book is a fully functional platform-independent open source Weka software for machine learning, available for free download. <br><br>The book is a major revision of the second edition that appeared in 2005. While the basic core remains the same, it has been updated to reflect the changes that have taken place over the last four or five years. The highlights for the updated new edition include completely revised technique sections; new chapter on Data Transformations, new chapter on Ensemble Learning, new chapter on Massive Data Sets, a new ?book release? version of the popular Weka machine learning open source software (developed by the authors and specific to the Third Edition); new material on ?multi-instance learning?; new information on ranking the classification, plus comprehensive updates and modernization throughout. All in all, approximately 100 pages of new material.<br> <br><br>* Thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques<br><br>* Algorithmic methods at the heart of successful data mining?including tired and true methods as well as leading edge methods<br><br>* Performance improvement techniques that work by transforming the input or output<br><br>* Downloadable Weka, a collection of machine learning algorithms for data mining tasks, including tools for data pre-processing, classification, regression, clustering, association rules, and visualization?in an updated, interactive interface. <br>
Автор: Marsland Название: Machine Learning ISBN: 1466583282 ISBN-13(EAN): 9781466583283 Издательство: Taylor&Francis Рейтинг: Цена: 5537 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This bestseller helps students understand the algorithms of machine learning. It puts them on a path toward mastering the relevant mathematics and statistics as well as the necessary programming and experimentation. Along with improved Python code, this second edition includes two new chapters on deep belief networks and Gaussian processes. It incorporates new material on the support vector machine, random forests, the perceptron convergence theorem, filters, and more. All of the code is available on the author’s website.
Описание: This book provides a thorough introduction to the most important topics in data mining and machine learning. All the topics covered have undergone rapid development and this treatment offers a modern perspective emphasizing the most recent contributions.
Описание: "Machine Learning and Data Mining for Computer Security" provides an overview of the current state of research in machine learning and data mining as it applies to problems in computer security.
Описание: This book constitutes the refereed proceedings of the Third International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2003, held in Leipzig, Germany, in July 2003.The 33 revised full papers presented together with two invited papers were carefully reviewed and selected from 75 submissions. The papers are organized in topical sections on decision trees; clustering and its applications; support vector machines; case-based reasoning; classification, retrieval, and feature Learning; discovery of frequent or sequential patterns; Bayesian models and methods; association rule mining; and applications.
Описание: Examining the connections between these two increasingly intertwined areas, this text presents a unifying, thorough, and accessible introduction to the basic ideas and latest developments in machine learning and bioinformatics. It describes the major problems in bioinformatics and the concepts and algorithms of machine learning. The authors demonstrate the capabilities of key machine learning techniques, such as hidden Markov models and artificial neural networks, and apply state-of-the-art techniques to bioinformatics problems in structural biology, cancer treatment, and proteomics. They also include exercises at the end of some chapters and offer instructional materials on their website.
Автор: Hardoon Название: Getting Started with Business Analytics ISBN: 1439896534 ISBN-13(EAN): 9781439896532 Издательство: Taylor&Francis Рейтинг: Цена: 4388 р. Наличие на складе: Поставка под заказ.
Описание: Helping you make sound decisions based on hard data, this self-contained guide provides an integrated framework of data mining in business analytics. It explores the contents, capabilities, and applications of business analytics without assuming any prior knowledge or technical skills. The authors describe business analytics from a non-commercial standpoint, demystify the main concepts and terminologies, and give many examples of real-world applications. They take you on a journey through this data-rich world, showing you how to deploy business analytics solutions in your organization.
Описание: This class-tested textbook will provide in-depth coverage of the fundamentals of machine learning, with an exploration of applications in information security. The book will cover malware detection, cryptography, and intrusion detection. The book will be relevant for students in machine learning and computer security courses.
Название: Event Mining ISBN: 1466568577 ISBN-13(EAN): 9781466568570 Издательство: Taylor&Francis Рейтинг: Цена: р. Наличие на складе: Поставка под заказ.
Описание: With a focus on computing system management, this book presents a variety of event mining approaches for improving the quality and efficiency of IT service and system management. It covers different components in the data-driven framework, from system monitoring and event generation to pattern discovery and summarization. The book explores recent developments in event mining, such as new clustering-based approaches, as well as various applications of event mining, including social media.
Описание: Data Mining is the science and technology of exploring large and complex bodies of data in order to discover useful patterns. It is extremely important, because it enables modeling and knowledge extraction from abundant data availability.Soft Computing for Knowledge Discovery and Data Mining introduces soft computing methods extending the envelope of problems that data mining can solve efficiently. This book presents practical soft-computing approaches in data mining.Soft Computing for Knowledge Discovery and Data Mining was written to provide investigators in the fields of information systems, engineering, computer science, statistics and management with a profound source for the role of soft computing in data mining. Practitioners and researchers will be particularly interested in the description of real world data mining projects performed with soft computing. The book is also suitable for advanced-level students in computer science.
Автор: Aggarwal Charu C. Название: Data Classification ISBN: 1466586745 ISBN-13(EAN): 9781466586741 Издательство: Taylor&Francis Рейтинг: Цена: 13599 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Research on the problem of classification tends to be fragmented across such areas as pattern recognition, database, data mining, and machine learning. Addressing the work of these different communities in a unified way, this book explores the underlying algorithms of classification as well as applications of classification in a variety of problem domains, including text, multimedia, social network, and biological data. It presents core methods in data classification, covers recent problem domains, and discusses advanced methods for enhancing the quality of the underlying classification results.
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