Multiple Criteria Decision Making
This book presents a broad range of innovative applications and case studies in all areas of management and engineering, including public administration, finance, marketing, engineering, transportation, and energy systems. It addresses issues related to problem structuring, preference modeling, and model construction, presenting a framework that provides clear decision-making support in practice. In addition, it includes hybrid and integrated techniques combining multiple criteria decision making (MCDM) with other analytical methods. The book reflects the growing impact of MCDM in the field of management science and operations research. Building on recent and established theoretical advances and presenting their applications in specific domains, it offers a comprehensive resource for researchers, graduate students and professionals alike.
Data Mining in Public and Private Sectors Organizational and Government Applications
The need for both organizations and government agencies to generate, collect, and utilize data in public and private sector activities is rapidly increasing, placing importance on the growth of data mining applications and tools. Data Mining in Public and Private Sectors: Organizational and Government Applications explores the manifestation of data mining and how it can be enhanced at various levels of management. This innovative publication provides relevant theoretical frameworks and the latest empirical research findings useful to governmental agencies, practicing managers, and academicians.
A été écrit sous une forme ou une autre pendant la plus grande partie de sa vie. Vous pouvez trouver autant d'inspiration de La Recherche Aussi informatif et amusant. Cliquez sur le bouton TÉLÉCHARGER ou Lire en ligne pour obtenir gratuitement le livre de titre $ gratuitement.
An Invitation to Cognitive Science Language
Rather than surveying theories and data in the manner characteristic of many introductory textbooks in the field, An Invitation to Cognitive Science employs a unique case study approach, presenting a focused research topic in some depth and relying on suggested readings to convey the breadth of views and results.
In this dystopian classic, a satire of Victorian society, the main character Higgs discovers an unknown country, the seeming utopia called Erewhon, Nowhere backwards with the "h" and "w" transposed. The starting chapters detailing the discovery of Erewhon were based on Butler's experiences in New Zealand as a young man. Butler was possibly the first to write about the idea that machines might one day develop consciousness through the process of Darwinian Selection. Dystopian Classic Editions publishes works of dystopian literature that have survived through the generations and been recognized as classic works of literature. A dystopian society is an imagined society in which the people are oppressed, however the government propagandizes the society as being a utopia or a perfect society. Typical themes in dystopian literature include public mistrust, police states, and overall unpleasantness for the citizens. Authors of dystopian works strive to present a worst-case scenario and negative depiction of the way things are in the story so as to make a criticism about a current situation in society and to call for a change. Each Dystopian Classic Edition selected for publication presents such a story.
A été écrit sous une forme ou une autre pendant la plus grande partie de sa vie. Vous pouvez trouver autant d'inspiration de TAL Aussi informatif et amusant. Cliquez sur le bouton TÉLÉCHARGER ou Lire en ligne pour obtenir gratuitement le livre de titre $ gratuitement.
Finding Groups in Data
The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists. "Cluster analysis is the increasingly important and practical subject of finding groupings in data. The authors set out to write a book for the user who does not necessarily have an extensive background in mathematics. They succeed very well." —Mathematical Reviews "Finding Groups in Data [is] a clear, readable, and interesting presentation of a small number of clustering methods. In addition, the book introduced some interesting innovations of applied value to clustering literature." —Journal of Classification "This is a very good, easy-to-read, and practical book. It has many nice features and is highly recommended for students and practitioners in various fields of study." —Technometrics An introduction to the practical application of cluster analysis, this text presents a selection of methods that together can deal with most applications. These methods are chosen for their robustness, consistency, and general applicability. This book discusses various types of data, including interval-scaled and binary variables as well as similarity data, and explains how these can be transformed prior to clustering.
Neural networks represent a powerful data processing technique that has reached maturity and broad application. When clearly understood and appropriately used, they are a mandatory component in the toolbox of any engineer who wants make the best use of the available data, in order to build models, make predictions, mine data, recognize shapes or signals, etc. Ranging from theoretical foundations to real-life applications, this book is intended to provide engineers and researchers with clear methodologies for taking advantage of neural networks in industrial, financial or banking applications, many instances of which are presented in the book. For the benefit of readers wishing to gain deeper knowledge of the topics, the book features appendices that provide theoretical details for greater insight, and algorithmic details for efficient programming and implementation. The chapters have been written by experts and edited to present a coherent and comprehensive, yet not redundant, practically oriented introduction.
Data Mining: Practical Machine Learning Tools and Techniques, Fourth Edition, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real-world data mining situations. This highly anticipated fourth edition of the most acclaimed work on data mining and machine learning teaches readers everything they need to know to get going, from preparing inputs, interpreting outputs, evaluating results, to the algorithmic methods at the heart of successful data mining approaches. Extensive updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including substantial new chapters on probabilistic methods and on deep learning. Accompanying the book is a new version of the popular WEKA machine learning software from the University of Waikato. Authors Witten, Frank, Hall, and Pal include today's techniques coupled with the methods at the leading edge of contemporary research. Please visit the book companion website at http://www.cs.waikato.ac.nz/ml/weka/book.html It contains Powerpoint slides for Chapters 1-12. This is a very comprehensive teaching resource, with many PPT slides covering each chapter of the book Online Appendix on the Weka workbench; again a very comprehensive learning aid for the open source software that goes with the book Table of contents, highlighting the many new sections in the 4th edition, along with reviews of the 1st edition, errata, etc. Provides a thorough grounding in machine learning concepts, as well as practical advice on applying the tools and techniques to data mining projects Presents concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods Includes a downloadable WEKA software toolkit, a comprehensive collection of machine learning algorithms for data mining tasks-in an easy-to-use interactive interface Includes open-access online courses that introduce practical applications of the material in the book
Simply put, quantum calculus is ordinary calculus without taking limits. This undergraduate text develops two types of quantum calculi, the q-calculus and the h-calculus. As this book develops quantum calculus along the lines of traditional calculus, the reader discovers, with a remarkable inevitability, many important notions and results of classical mathematics. This book is written at the level of a first course in calculus and linear algebra and is aimed at undergraduate and beginning graduate students in mathematics, computer science, and physics. It is based on lectures and seminars given by Professor Kac over the last few years at MIT. Victor Kac is Professor of Mathematics at MIT. He is an author of 4 books and over a hundred research papers. He was awarded the Wigner Medal for his work on Kac-Moody algebras that has numerous applications to mathematics and theoretical physics. He is a honorary member of the Moscow Mathematical Society. Pokman Cheung graduated from MIT in 2001 after three years of undergraduate studies. He is presently a graduate student at Stanford University.