On Optimal Subset Selection Procedures
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Optimal Subset Selection
Author | : David Boyce |
Publisher | : Springer Science & Business Media |
Total Pages | : 203 |
Release | : 2013-03-08 |
Genre | : Mathematics |
ISBN | : 3642463118 |
In the course of one's research, the expediency of meeting contractual and other externally imposed deadlines too often seems to take priority over what may be more significant research findings in the longer run. Such is the case with this volume which, despite our best intentions, has been put aside time and again since 1971 in favor of what seemed to be more urgent matters. Despite this delay, to our knowledge the principal research results and documentation presented here have not been superseded by other publications. The background of this endeavor may be of some historical interest, especially to those who agree that research is not a straightforward, mechanistic process whose outcome or even direction is known in ad vance. In the process of this brief recounting, we would like to express our gratitude to those individuals and organizations who facilitated and supported our efforts. We were introduced to the Beale, Kendall and Mann algorithm, the source of all our efforts, quite by chance. Professor Britton Harris suggested to me in April 1967 that I might like to attend a CEIR half-day seminar on optimal regression being given by Professor M. G. Kendall in Washington. D. C. I agreed that the topic seemed interesting and went along. Had it not been for Harris' suggestion and financial support, this work almost certainly would have never begun.
Machine Learning Under a Modern Optimization Lens
Author | : Dimitris Bertsimas |
Publisher | : |
Total Pages | : 589 |
Release | : 2019 |
Genre | : Machine learning |
ISBN | : 9781733788502 |
Feature Engineering and Selection
Author | : Max Kuhn |
Publisher | : CRC Press |
Total Pages | : 266 |
Release | : 2019-07-25 |
Genre | : Business & Economics |
ISBN | : 1351609467 |
The process of developing predictive models includes many stages. Most resources focus on the modeling algorithms but neglect other critical aspects of the modeling process. This book describes techniques for finding the best representations of predictors for modeling and for nding the best subset of predictors for improving model performance. A variety of example data sets are used to illustrate the techniques along with R programs for reproducing the results.
Multiple Decision Procedures
Author | : Shanti S. Gupta |
Publisher | : SIAM |
Total Pages | : 592 |
Release | : 2002-01-01 |
Genre | : Mathematics |
ISBN | : 0898715326 |
An encyclopaedic coverage of the literature in the area of ranking and selection procedures. It also deals with the estimation of unknown ordered parameters. This book can serve as a text for a graduate topics course in ranking and selection. It is also a valuable reference for researchers and practitioners.
Feature Extraction, Construction and Selection
Author | : Huan Liu |
Publisher | : Springer Science & Business Media |
Total Pages | : 418 |
Release | : 2012-12-06 |
Genre | : Computers |
ISBN | : 1461557259 |
There is broad interest in feature extraction, construction, and selection among practitioners from statistics, pattern recognition, and data mining to machine learning. Data preprocessing is an essential step in the knowledge discovery process for real-world applications. This book compiles contributions from many leading and active researchers in this growing field and paints a picture of the state-of-art techniques that can boost the capabilities of many existing data mining tools. The objective of this collection is to increase the awareness of the data mining community about the research of feature extraction, construction and selection, which are currently conducted mainly in isolation. This book is part of our endeavor to produce a contemporary overview of modern solutions, to create synergy among these seemingly different branches, and to pave the way for developing meta-systems and novel approaches. Even with today's advanced computer technologies, discovering knowledge from data can still be fiendishly hard due to the characteristics of the computer generated data. Feature extraction, construction and selection are a set of techniques that transform and simplify data so as to make data mining tasks easier. Feature construction and selection can be viewed as two sides of the representation problem.
Forecasting: principles and practice
Author | : Rob J Hyndman |
Publisher | : OTexts |
Total Pages | : 380 |
Release | : 2018-05-08 |
Genre | : Business & Economics |
ISBN | : 0987507117 |
Forecasting is required in many situations. Stocking an inventory may require forecasts of demand months in advance. Telecommunication routing requires traffic forecasts a few minutes ahead. Whatever the circumstances or time horizons involved, forecasting is an important aid in effective and efficient planning. This textbook provides a comprehensive introduction to forecasting methods and presents enough information about each method for readers to use them sensibly.
Flexible Imputation of Missing Data, Second Edition
Author | : Stef van Buuren |
Publisher | : CRC Press |
Total Pages | : 444 |
Release | : 2018-07-17 |
Genre | : Mathematics |
ISBN | : 0429960352 |
Missing data pose challenges to real-life data analysis. Simple ad-hoc fixes, like deletion or mean imputation, only work under highly restrictive conditions, which are often not met in practice. Multiple imputation replaces each missing value by multiple plausible values. The variability between these replacements reflects our ignorance of the true (but missing) value. Each of the completed data set is then analyzed by standard methods, and the results are pooled to obtain unbiased estimates with correct confidence intervals. Multiple imputation is a general approach that also inspires novel solutions to old problems by reformulating the task at hand as a missing-data problem. This is the second edition of a popular book on multiple imputation, focused on explaining the application of methods through detailed worked examples using the MICE package as developed by the author. This new edition incorporates the recent developments in this fast-moving field. This class-tested book avoids mathematical and technical details as much as possible: formulas are accompanied by verbal statements that explain the formula in accessible terms. The book sharpens the reader’s intuition on how to think about missing data, and provides all the tools needed to execute a well-grounded quantitative analysis in the presence of missing data.
Advances in Statistical Decision Theory and Applications
Author | : S. Panchapakesan |
Publisher | : Springer Science & Business Media |
Total Pages | : 478 |
Release | : 2012-12-06 |
Genre | : Mathematics |
ISBN | : 1461223083 |
Shanti S. Gupta has made pioneering contributions to ranking and selection theory; in particular, to subset selection theory. His list of publications and the numerous citations his publications have received over the last forty years will amply testify to this fact. Besides ranking and selection, his interests include order statistics and reliability theory. The first editor's association with Shanti Gupta goes back to 1965 when he came to Purdue to do his Ph.D. He has the good fortune of being a student, a colleague and a long-standing collaborator of Shanti Gupta. The second editor's association with Shanti Gupta began in 1978 when he started his research in the area of order statistics. During the past twenty years, he has collaborated with Shanti Gupta on several publications. We both feel that our lives have been enriched by our association with him. He has indeed been a friend, philosopher and guide to us.
Identity Theft: Breakthroughs in Research and Practice
Author | : Management Association, Information Resources |
Publisher | : IGI Global |
Total Pages | : 453 |
Release | : 2016-09-27 |
Genre | : Computers |
ISBN | : 1522508090 |
The preservation of private data is a main concern of governments, organizations, and individuals alike. For individuals, a breach in personal information can mean dire consequences for an individual’s finances, medical information, and personal property. Identity Theft: Breakthroughs in Research and Practice highlights emerging perspectives and critical insights into the preservation of personal data and the complications that can arise when one’s identity is compromised. This critical volume features key research on methods and technologies for protection, the problems associated with identity theft, and outlooks for the future. This publication is an essential resource for information security professionals, researchers, and graduate-level students in the fields of criminal science, business, and computer science.