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Multiple Classification Analysis
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Book Synopsis Multiple Classification Analysis by : Frank M. Andrews
Download or read book Multiple Classification Analysis written by Frank M. Andrews and published by University of Michigan Press. This book was released on 1973 with total page 128 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Statistical Models for Causal Analysis by : Robert D. Retherford
Download or read book Statistical Models for Causal Analysis written by Robert D. Retherford and published by John Wiley & Sons. This book was released on 2011-02-01 with total page 274 pages. Available in PDF, EPUB and Kindle. Book excerpt: Simplifies the treatment of statistical inference focusing on how to specify and interpret models in the context of testing causal theories. Simple bivariate regression, multiple regression, multiple classification analysis, path analysis, logit regression, multinomial logit regression and survival models are among the subjects covered. Features an appendix of computer programs (for major statistical packages) that are used to generate illustrative examples contained in the chapters.
Book Synopsis Multilabel Classification by : Francisco Herrera
Download or read book Multilabel Classification written by Francisco Herrera and published by Springer. This book was released on 2016-08-09 with total page 200 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers a comprehensive review of multilabel techniques widely used to classify and label texts, pictures, videos and music in the Internet. A deep review of the specialized literature on the field includes the available software needed to work with this kind of data. It provides the user with the software tools needed to deal with multilabel data, as well as step by step instruction on how to use them. The main topics covered are: • The special characteristics of multi-labeled data and the metrics available to measure them.• The importance of taking advantage of label correlations to improve the results.• The different approaches followed to face multi-label classification.• The preprocessing techniques applicable to multi-label datasets.• The available software tools to work with multi-label data. This book is beneficial for professionals and researchers in a variety of fields because of the wide range of potential applications for multilabel classification. Besides its multiple applications to classify different types of online information, it is also useful in many other areas, such as genomics and biology. No previous knowledge about the subject is required. The book introduces all the needed concepts to understand multilabel data characterization, treatment and evaluation.
Book Synopsis Multiple Classification Analysis by :
Download or read book Multiple Classification Analysis written by and published by . This book was released on 1975 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Practical Guide To Principal Component Methods in R by : Alboukadel KASSAMBARA
Download or read book Practical Guide To Principal Component Methods in R written by Alboukadel KASSAMBARA and published by STHDA. This book was released on 2017-08-23 with total page 171 pages. Available in PDF, EPUB and Kindle. Book excerpt: Although there are several good books on principal component methods (PCMs) and related topics, we felt that many of them are either too theoretical or too advanced. This book provides a solid practical guidance to summarize, visualize and interpret the most important information in a large multivariate data sets, using principal component methods in R. The visualization is based on the factoextra R package that we developed for creating easily beautiful ggplot2-based graphs from the output of PCMs. This book contains 4 parts. Part I provides a quick introduction to R and presents the key features of FactoMineR and factoextra. Part II describes classical principal component methods to analyze data sets containing, predominantly, either continuous or categorical variables. These methods include: Principal Component Analysis (PCA, for continuous variables), simple correspondence analysis (CA, for large contingency tables formed by two categorical variables) and Multiple CA (MCA, for a data set with more than 2 categorical variables). In Part III, you'll learn advanced methods for analyzing a data set containing a mix of variables (continuous and categorical) structured or not into groups: Factor Analysis of Mixed Data (FAMD) and Multiple Factor Analysis (MFA). Part IV covers hierarchical clustering on principal components (HCPC), which is useful for performing clustering with a data set containing only categorical variables or with a mixed data of categorical and continuous variables.
Book Synopsis AI 2008: Advances in Artificial Intelligence by : Wayne Wobcke
Download or read book AI 2008: Advances in Artificial Intelligence written by Wayne Wobcke and published by Springer Science & Business Media. This book was released on 2008-11-13 with total page 631 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 21th Australasian Joint Conference on Artificial Intelligence, AI 2008, held in Auckland, New Zealand, in December 2008. The 42 revised full papers and 21 revised short papers presented together with 1 invited lecture were carefully reviewed and selected from 143 submissions. The papers are organized in topical sections on knowledge representation, constraints, planning, grammar and language processing, statistical learning, machine learning, data mining, knowledge discovery, soft computing, vision and image processing, and AI applications.
Book Synopsis Supervised Machine Learning for Text Analysis in R by : Emil Hvitfeldt
Download or read book Supervised Machine Learning for Text Analysis in R written by Emil Hvitfeldt and published by CRC Press. This book was released on 2021-10-22 with total page 402 pages. Available in PDF, EPUB and Kindle. Book excerpt: Text data is important for many domains, from healthcare to marketing to the digital humanities, but specialized approaches are necessary to create features for machine learning from language. Supervised Machine Learning for Text Analysis in R explains how to preprocess text data for modeling, train models, and evaluate model performance using tools from the tidyverse and tidymodels ecosystem. Models like these can be used to make predictions for new observations, to understand what natural language features or characteristics contribute to differences in the output, and more. If you are already familiar with the basics of predictive modeling, use the comprehensive, detailed examples in this book to extend your skills to the domain of natural language processing. This book provides practical guidance and directly applicable knowledge for data scientists and analysts who want to integrate unstructured text data into their modeling pipelines. Learn how to use text data for both regression and classification tasks, and how to apply more straightforward algorithms like regularized regression or support vector machines as well as deep learning approaches. Natural language must be dramatically transformed to be ready for computation, so we explore typical text preprocessing and feature engineering steps like tokenization and word embeddings from the ground up. These steps influence model results in ways we can measure, both in terms of model metrics and other tangible consequences such as how fair or appropriate model results are.
Book Synopsis Multiple Classifier Systems by : Carlo Sansone
Download or read book Multiple Classifier Systems written by Carlo Sansone and published by Springer Science & Business Media. This book was released on 2011-06-14 with total page 382 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 10th International Workshop on Multiple Classifier Systems, MCS 2011, held in Naples, Italy, in June 2011. The 36 revised papers presented together with two invited papers were carefully reviewed and selected from more than 50 submissions. The contributions are organized into sessions dealing with classifier ensembles; trees and forests; one-class classifiers; multiple kernels; classifier selection; sequential combination; ECOC; diversity; clustering; biometrics; and computer security.
Book Synopsis Statistical Advances in Biosciences and Bioinformatics by : International Biometric Society. Indian Region. Conference
Download or read book Statistical Advances in Biosciences and Bioinformatics written by International Biometric Society. Indian Region. Conference and published by Allied Publishers. This book was released on 2006 with total page 324 pages. Available in PDF, EPUB and Kindle. Book excerpt: Papers presented at the conference, held during 23-27 Nov. 2003, at Banaras Hindu University, Varanasi.
Book Synopsis Years for Decision by : Roderick, Roger D.
Download or read book Years for Decision written by Roderick, Roger D. and published by . This book was released on 1976 with total page 346 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Years for Decision by : Roger Duane Roderick
Download or read book Years for Decision written by Roger Duane Roderick and published by . This book was released on 1976 with total page 472 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Flexible Imputation of Missing Data, Second Edition by : Stef van Buuren
Download or read book Flexible Imputation of Missing Data, Second Edition written by Stef van Buuren and published by CRC Press. This book was released on 2018-07-17 with total page 444 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics by : Elena Marchiori
Download or read book Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics written by Elena Marchiori and published by Springer Science & Business Media. This book was released on 2007-04-02 with total page 311 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 5th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, EvoBIO 2007, held in Valencia, Spain, April 2007. Coverage brings together experts in computer science with experts in bioinformatics and the biological sciences. It presents contributions on fundamental and theoretical issues along with papers dealing with different applications areas.
Download or read book Years for Decision written by and published by . This book was released on 1971 with total page 876 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis From Data and Information Analysis to Knowledge Engineering by : Myra Spiliopoulou
Download or read book From Data and Information Analysis to Knowledge Engineering written by Myra Spiliopoulou and published by Springer Science & Business Media. This book was released on 2006-04-20 with total page 780 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume collects revised versions of papers presented at the 29th Annual Conference of the Gesellschaft für Klassifikation, the German Classification Society, held at the Otto-von-Guericke-University of Magdeburg, Germany, in March 2005. In addition to traditional subjects like Classification, Clustering, and Data Analysis, converage extends to a wide range of topics relating to Computer Science: Text Mining, Web Mining, Fuzzy Data Analysis, IT Security, Adaptivity and Personalization, and Visualization.
Author :Suchart Prasithrathsin Publisher :Institute of Southeast Asian Studies ISBN 13 :9971902176 Total Pages :86 pages Book Rating :4.9/5 (719 download)
Book Synopsis Culture and Fertility by : Suchart Prasithrathsin
Download or read book Culture and Fertility written by Suchart Prasithrathsin and published by Institute of Southeast Asian Studies. This book was released on 1980 with total page 86 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper, exploring the relationship between culture and fertility in Thailand, cites empirical evidence showing that each ethnic group's birth control practice is affected differently by different kinds of variables. For the Thais, birth control pactice is related to women's education and the number of live births. For the Chinese, place of residence, the level of household income and the number of children ever born are significantly related to the dependent variable. For the Moslems, none of these variables nor any of the other independent variables and covariates is significantly related to the practice of birth control. More research is needed in this area to find out what factors are most related to the adoption of birth control by the Muslims.
Book Synopsis Statistical Regression and Classification by : Norman Matloff
Download or read book Statistical Regression and Classification written by Norman Matloff and published by CRC Press. This book was released on 2017-09-19 with total page 439 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical Regression and Classification: From Linear Models to Machine Learning takes an innovative look at the traditional statistical regression course, presenting a contemporary treatment in line with today's applications and users. The text takes a modern look at regression: * A thorough treatment of classical linear and generalized linear models, supplemented with introductory material on machine learning methods. * Since classification is the focus of many contemporary applications, the book covers this topic in detail, especially the multiclass case. * In view of the voluminous nature of many modern datasets, there is a chapter on Big Data. * Has special Mathematical and Computational Complements sections at ends of chapters, and exercises are partitioned into Data, Math and Complements problems. * Instructors can tailor coverage for specific audiences such as majors in Statistics, Computer Science, or Economics. * More than 75 examples using real data. The book treats classical regression methods in an innovative, contemporary manner. Though some statistical learning methods are introduced, the primary methodology used is linear and generalized linear parametric models, covering both the Description and Prediction goals of regression methods. The author is just as interested in Description applications of regression, such as measuring the gender wage gap in Silicon Valley, as in forecasting tomorrow's demand for bike rentals. An entire chapter is devoted to measuring such effects, including discussion of Simpson's Paradox, multiple inference, and causation issues. Similarly, there is an entire chapter of parametric model fit, making use of both residual analysis and assessment via nonparametric analysis. Norman Matloff is a professor of computer science at the University of California, Davis, and was a founder of the Statistics Department at that institution. His current research focus is on recommender systems, and applications of regression methods to small area estimation and bias reduction in observational studies. He is on the editorial boards of the Journal of Statistical Computation and the R Journal. An award-winning teacher, he is the author of The Art of R Programming and Parallel Computation in Data Science: With Examples in R, C++ and CUDA.