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.
Автор: David L. Olson; Desheng Wu Название: Predictive Data Mining Models ISBN: 9811396639 ISBN-13(EAN): 9789811396632 Издательство: Springer Рейтинг: Цена: 11179.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
This book provides an overview of predictive methods demonstrated by open source software modeling with Rattle (R’) and WEKA. Knowledge management involves application of human knowledge (epistemology) with the technological advances of our current society (computer systems) and big data, both in terms of collecting data and in analyzing it. We see three types of analytic tools. Descriptive analytics focus on reports of what has happened. Predictive analytics extend statistical and/or artificial intelligence to provide forecasting capability. It also includes classification modeling. Prescriptive analytics applies quantitative models to optimize systems, or at least to identify improved systems. Data mining includes descriptive and predictive modeling. Operations research includes all three. This book focuses on prescriptive analytics.
The book seeks to provide simple explanations and demonstration of some descriptive tools. This second edition provides more examples of big data impact, updates the content on visualization, clarifies some points, and expands coverage of association rules and cluster analysis. Chapter 1 gives an overview in the context of knowledge management. Chapter 2 discusses some basic data types. Chapter 3 covers fundamentals time series modeling tools, and Chapter 4 provides demonstration of multiple regression modeling. Chapter 5 demonstrates regression tree modeling. Chapter 6 presents autoregressive/integrated/moving average models, as well as GARCH models. Chapter 7 covers the set of data mining tools used in classification, to include special variants support vector machines, random forests, and boosting.
Models are demonstrated using business related data. The style of the book is intended to be descriptive, seeking to explain how methods work, with some citations, but without deep scholarly reference. The data sets and software are all selected for widespread availability and access by any reader with computer links.
Автор: Koller Daphne, Friedman Nir Название: Probabilistic Graphical Models: Principles and Techniques ISBN: 0262013193 ISBN-13(EAN): 9780262013192 Издательство: MIT Press Рейтинг: Цена: 21161.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
A general framework for constructing and using probabilistic models of complex systems that would enable a computer to use available information for making decisions.
Most tasks require a person or an automated system to reason -- to reach conclusions based on available information. The framework of probabilistic graphical models, presented in this book, provides a general approach for this task. The approach is model-based, allowing interpretable models to be constructed and then manipulated by reasoning algorithms. These models can also be learned automatically from data, allowing the approach to be used in cases where manually constructing a model is difficult or even impossible. Because uncertainty is an inescapable aspect of most real-world applications, the book focuses on probabilistic models, which make the uncertainty explicit and provide models that are more faithful to reality.
Probabilistic Graphical Models discusses a variety of models, spanning Bayesian networks, undirected Markov networks, discrete and continuous models, and extensions to deal with dynamical systems and relational data. For each class of models, the text describes the three fundamental cornerstones: representation, inference, and learning, presenting both basic concepts and advanced techniques. Finally, the book considers the use of the proposed framework for causal reasoning and decision making under uncertainty. The main text in each chapter provides the detailed technical development of the key ideas. Most chapters also include boxes with additional material: skill boxes, which describe techniques; case study boxes, which discuss empirical cases related to the approach described in the text, including applications in computer vision, robotics, natural language understanding, and computational biology; and concept boxes, which present significant concepts drawn from the material in the chapter. Instructors (and readers) can group chapters in various combinations, from core topics to more technically advanced material, to suit their particular needs.
Описание: Written by an Oracle ACE Director, this book shows you how to use Oracle Data Miner to create and deploy advanced data mining models and integrate your models into applications to automate the discovery and distribution of business intelligence predictions throughout the enterprise.
Описание: A successful administrator is one who applies suitable or appropriate leadership styles in various situations or contexts. It is crucial to investigate how effective administrators lead their organizations in challenging and difficult times, as well as promote the accomplishments of their organization.Predictive Models for School Leadership and Practices is an essential reference source that discusses academic administration as well as administrative effectiveness in achieving organizational goals. Featuring research on topics such as teacher collaboration, school crisis management, and ITC integration, this book is ideally designed for principals, researchers, academics, educational policymakers, and teachers seeking coverage on academic leadership and leadership models.
Автор: Camacho Название: Model Predictive Control ISBN: 1852336943 ISBN-13(EAN): 9781852336943 Издательство: Springer Рейтинг: Цена: 9781.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The second edition of "Model Predictive Control" provides a thorough introduction to theoretical and practical aspects of the most commonly used MPC strategies. It bridges the gap between the powerful but often abstract techniques of control researchers and the more empirical approach of practitioners.
Описание: Structured into four sections, this book offers not only basic information on the action mechanisms of UV radiation on ecosystems and various biological systems, but also a picture of the possible scenarios of the long-term global increase of environmental UV radiation, placing an emphasis on the research aspects.
The DNA sequencing of a series of living organisms has elucidated many biological problems. But the internal atomic and electronic evolution of DNA remains to be mapped in detail. RNA and DNA now appear to be the prime determinants of biological evolution leading to the sudden appearance of novel organism structures and functions that emerge "ready made" as a surprise to the organism. This has been demonstrated by the manipulation of genes that led to the sudden production of additional complete wings and legs in flies and birds. The study of this internal atomic construction of macromolecules is being investigated at the large electron accelerators such as the MAX IV Synchrotron Radiation Laboratory, Lund University, Sweden.
The periodicity of the chemical elements is well known from its iconic Table. Significantly, this periodicity can now be seen to extend to the properties of living organisms. Biological properties as different as: flight, vision, luminescence and regeneration, as well as others, show unexpectedly periodic emergence. They resurface, without previous announcement, in most unrelated plant and animal families and they emerge irrespective of whether the organism is a simple invertebrate or a most complex mammal.
Moreover, this periodicity does not necessarily start at the cell or DNA levels but appears initially in crystals and minerals, where it is shown to be a pure atomic and electronic process, e.g. in luminescence and regeneration.
The assembled molecular evidence led to the construction of Periodic Tables of living organisms, placing them in a position comparable to the periodicity of the chemical elements. Surprisingly, there are striking resemblances between the periodicities of the chemical elements and those of living organisms. In addition, the two types of Tables increase our insight into the events directing atomic evolution since the periodic law established in chemical elements turns out to be applicable to the periodicity of living organisms. The new Periodic Tables introduce a predictive capacity in biological evolution that before was hardly contemplated.
Eric Scerri, from the Department of Chemistry and Biochemistry, California University, Los Angeles, who is the Author of the book "The Periodic Table. Its Story and its Significance," Oxford University Press, stated in an e-mail that "Professor Lima-de-Faria's book is wonderful and a pioneering work."
Описание: This book gathers contributions addressing issues related to the analysis of composite structures, whose most relevant common thread is augmented numerical efficiency, which is more accurate for given computational costs than existing methods and methodologies. It first presents structural theories to deal with the anisotropy of composites and to embed multifield and nonlinear effects to extend design capabilities and provide methods of augmenting the fidelity of structural theories and lowering computational costs, including the finite element method. The second part of the book focuses on damage analysis; the multiscale and multicomponent nature of composites leads to extremely complex failure mechanisms, and predictive tools require physics-based models to reduce the need for fitting and tuning based on costly and lengthy experiments, and to lower computational costs; furthermore the correct monitoring of in-service damage is decisive in the context of damage tolerance. The third part then presents recent advances in embedding characterization and manufacturing effects in virtual testing. The book summarizes the outcomes of the FULLCOMP (FULLy integrated analysis, design, manufacturing, and health-monitoring of COMPosite structures) research project.
ООО "Логосфера " Тел:+7(495) 980-12-10 www.logobook.ru
