Download Discrimination And Classification full books in PDF, epub, and Kindle. Read online free Discrimination And Classification ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!
| Author | : David J. Hand |
| Publisher | : |
| Total Pages | : 240 |
| Release | : 1981-11-04 |
| Genre | : Mathematics |
| ISBN | : |
Distribution-free methods; Parameterized distributions; Linear discriminant functions; Discrete variables; Variable selection; Cluster analysis; Miscellaneous topics.
| Author | : Bart Custers |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 370 |
| Release | : 2012-08-11 |
| Genre | : Technology & Engineering |
| ISBN | : 3642304877 |
Vast amounts of data are nowadays collected, stored and processed, in an effort to assist in making a variety of administrative and governmental decisions. These innovative steps considerably improve the speed, effectiveness and quality of decisions. Analyses are increasingly performed by data mining and profiling technologies that statistically and automatically determine patterns and trends. However, when such practices lead to unwanted or unjustified selections, they may result in unacceptable forms of discrimination. Processing vast amounts of data may lead to situations in which data controllers know many of the characteristics, behaviors and whereabouts of people. In some cases, analysts might know more about individuals than these individuals know about themselves. Judging people by their digital identities sheds a different light on our views of privacy and data protection. This book discusses discrimination and privacy issues related to data mining and profiling practices. It provides technological and regulatory solutions, to problems which arise in these innovative contexts. The book explains that common measures for mitigating privacy and discrimination, such as access controls and anonymity, fail to properly resolve privacy and discrimination concerns. Therefore, new solutions, focusing on technology design, transparency and accountability are called for and set forth.
| Author | : Bernard Flury |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 723 |
| Release | : 2013-03-09 |
| Genre | : Mathematics |
| ISBN | : 1475727658 |
A comprehensive and self-contained introduction to the field, carefully balancing mathematical theory and practical applications. It starts at an elementary level, developing concepts of multivariate distributions from first principles. After a chapter on the multivariate normal distribution reviewing the classical parametric theory, methods of estimation are explored using the plug-in principles as well as maximum likelihood. Two chapters on discrimination and classification, including logistic regression, form the core of the book, followed by methods of testing hypotheses developed from heuristic principles, likelihood ratio tests and permutation tests. Finally, the powerful self-consistency principle is used to introduce principal components as a method of approximation, rounded off by a chapter on finite mixture analysis.
| Author | : David J. Hand |
| Publisher | : |
| Total Pages | : 236 |
| Release | : 1981-11-04 |
| Genre | : Mathematics |
| ISBN | : |
Distribution-free methods; Parameterized distributions; Linear discriminant functions; Discrete variables; Variable selection; Cluster analysis; Miscellaneous topics.
| Author | : Parimal Mukhopadhyay |
| Publisher | : World Scientific |
| Total Pages | : 568 |
| Release | : 2009 |
| Genre | : Mathematics |
| ISBN | : 9812791752 |
"This textbook presents a classical approach to some techniques of multivariate analysis in a simple and transparent manner. It offers clear and concise development of the concepts; interpretation of the output of the analysis; and criteria for selection of the methods, taking into account the strengths and weaknesses of each." "This book is ideal as an advanced textbook for graduate students in statistics and other disciplines like social, biological and physical sciences. It will also be of benefit to professional statisticians." --Book Jacket.
| Author | : National Research Council |
| Publisher | : National Academies Press |
| Total Pages | : 335 |
| Release | : 2004-07-24 |
| Genre | : Social Science |
| ISBN | : 0309091268 |
Many racial and ethnic groups in the United States, including blacks, Hispanics, Asians, American Indians, and others, have historically faced severe discriminationâ€"pervasive and open denial of civil, social, political, educational, and economic opportunities. Today, large differences among racial and ethnic groups continue to exist in employment, income and wealth, housing, education, criminal justice, health, and other areas. While many factors may contribute to such differences, their size and extent suggest that various forms of discriminatory treatment persist in U.S. society and serve to undercut the achievement of equal opportunity. Measuring Racial Discrimination considers the definition of race and racial discrimination, reviews the existing techniques used to measure racial discrimination, and identifies new tools and areas for future research. The book conducts a thorough evaluation of current methodologies for a wide range of circumstances in which racial discrimination may occur, and makes recommendations on how to better assess the presence and effects of discrimination.
| Author | : Mohamed Kamel |
| Publisher | : Springer |
| Total Pages | : 828 |
| Release | : 2013-06-05 |
| Genre | : Computers |
| ISBN | : 3642390943 |
This book constitutes the thoroughly refereed proceedings of the 10th International Conference on Image Analysis and Recognition, ICIAR 2013, held in Póvoa do Varzim, Portugal, in June 2013, The 92 revised full papers presented were carefully reviewed and selected from 177 submissions. The papers are organized in topical sections on biometrics: behavioral; biometrics: physiological; classification and regression; object recognition; image processing and analysis: representations and models, compression, enhancement , feature detection and segmentation; 3D image analysis; tracking; medical imaging: image segmentation, image registration, image analysis, coronary image analysis, retinal image analysis, computer aided diagnosis, brain image analysis; cell image analysis; RGB-D camera applications; methods of moments; applications.
| Author | : Deborah Hellman |
| Publisher | : Harvard University Press |
| Total Pages | : 217 |
| Release | : 2011-03-11 |
| Genre | : Law |
| ISBN | : 0674060296 |
A law requires black bus passengers to sit in the back of the bus. The U.S. Food and Drug Administration approves a drug for use by black heart failure patients. A state refuses to license drivers under age 16. A company avoids hiring women between the ages of 20 and 40. We routinely draw distinctions among people on the basis of characteristics that they possess or lack. While some distinctions are benign, many are morally troubling. In this boldly conceived book, Deborah Hellman develops a much-needed general theory of discrimination. She demonstrates that many familiar ideas about when discrimination is wrongÑwhen it is motivated by prejudice, grounded in stereotypes, or simply departs from merit-based decision-makingÑwonÕt adequately explain our widely shared intuitions. Hellman argues that, in the end, distinguishing among people on the basis of traits is wrong when it demeans any of the people affected. She deftly explores the question of how we determine what is in fact demeaning. Claims of wrongful discrimination are among the most common moral claims asserted in public and private life. Yet the roots of these claims are often left unanalyzed. When Is Discrimination Wrong? explores what it means to treat people as equals and thus takes up a central problem of democracy.
| Author | : National Academies of Sciences, Engineering, and Medicine |
| Publisher | : National Academies Press |
| Total Pages | : 583 |
| Release | : 2017-04-27 |
| Genre | : Medical |
| ISBN | : 0309452961 |
In the United States, some populations suffer from far greater disparities in health than others. Those disparities are caused not only by fundamental differences in health status across segments of the population, but also because of inequities in factors that impact health status, so-called determinants of health. Only part of an individual's health status depends on his or her behavior and choice; community-wide problems like poverty, unemployment, poor education, inadequate housing, poor public transportation, interpersonal violence, and decaying neighborhoods also contribute to health inequities, as well as the historic and ongoing interplay of structures, policies, and norms that shape lives. When these factors are not optimal in a community, it does not mean they are intractable: such inequities can be mitigated by social policies that can shape health in powerful ways. Communities in Action: Pathways to Health Equity seeks to delineate the causes of and the solutions to health inequities in the United States. This report focuses on what communities can do to promote health equity, what actions are needed by the many and varied stakeholders that are part of communities or support them, as well as the root causes and structural barriers that need to be overcome.
| Author | : Peter A. Flach |
| Publisher | : Springer |
| Total Pages | : 867 |
| Release | : 2012-08-15 |
| Genre | : Computers |
| ISBN | : 9783642334856 |
This two-volume set LNAI 7523 and LNAI 7524 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2012, held in Bristol, UK, in September 2012. The 105 revised research papers presented together with 5 invited talks were carefully reviewed and selected from 443 submissions. The final sections of the proceedings are devoted to Demo and Nectar papers. The Demo track includes 10 papers (from 19 submissions) and the Nectar track includes 4 papers (from 14 submissions). The papers grouped in topical sections on association rules and frequent patterns; Bayesian learning and graphical models; classification; dimensionality reduction, feature selection and extraction; distance-based methods and kernels; ensemble methods; graph and tree mining; large-scale, distributed and parallel mining and learning; multi-relational mining and learning; multi-task learning; natural language processing; online learning and data streams; privacy and security; rankings and recommendations; reinforcement learning and planning; rule mining and subgroup discovery; semi-supervised and transductive learning; sensor data; sequence and string mining; social network mining; spatial and geographical data mining; statistical methods and evaluation; time series and temporal data mining; and transfer learning.
