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students of data mining and machine learning, Ensemble Classification Methods with Applications in R puts the focus on two topics in machine;
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Ensemble Learning with R begins with the important statistical resampling methods. You will then walk through the central trilogy of ensemble;
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improvements are achieved. In addition, it covers the techniques with which such methods can be made more effective. A formal classification of these;
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This updated compendium provides a methodical introduction with a coherent and unified repository of ensemble methods, theories, trends;
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of classifier ensemble methods. Thoroughly updated, with MATLAB(R) code and practice data sets throughout, Combining Pattern Classifiers includes;
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Friederichs, Wahl, and Buschow (Chapter 5). The second section concludes with a discussion of forecast verification methods devised specifically for;
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of ensemble machine learning such as boosting, bagging, and stacking. The Ensemble Machine Learning Cookbook will start by getting you acquainted with;
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and business applications. Fuzzy classifiers use fuzzy rules and do not require assumptions common to statistical classification. Rough set;
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classification methods with a single, running example, highlighting similarities and differences between methods Presents R source code which shows how;
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(SUEMA) that was held in conjunction with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery;
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Comprehensive Coverage of the Entire Area of ClassificationResearch on the problem of classification tends to be fragmented across such;
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concludes with insight on variations of the classification process. It discusses ensembles, rare-class learning, distance function learning, active;
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This book explores internet applications in which a crucial role is played by classification, such as spam filtering, recommender systems;
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This book explores internet applications in which a crucial role is played by classification, such as spam filtering, recommender systems;
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performing statistical calculations in IBM(R) SPSS (R) along with additional exercises to reinforce concepts in the text. It follows the main text;
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of bootstrap methods. An introduction to R programming provides the needed preparation to work with the numerous exercises and applications presented;
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, classification of images, analysis of big data, and intelligent decision-making, this book provides readers with the latest, cutting-edge research in one;
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Data Mining for Business Analytics: Concepts, Techniques, and Applications with JMP Pro(R) presents an applied and interactive approach to;
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This book describes breath signal processing technologies and their applications in medical sample classification and diagnosis. First, it;
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experience with R is not necessary. New to this edition are chapters devoted to longitudinal studies and the clustering of large data. It is an;
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detection; applications; classification; clustering and unsupervised learning; deep learning; ensemble methods; and evaluation. Part II: graphs;
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Nonlinear Optimization in Electrical Engineering with Applications in MATLAB (R) provides an introductory course on nonlinear optimization;
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clustering and classifying different types of large datasets, including time series data. Time Series Clustering and Classification includes relevant;
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clustering and classifying different types of large datasets, including time series data. Time Series Clustering and Classification includes;
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This edited volume on the latest advances in data science covers a wide range of topics in the context of data analysis and classification;
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contributions presented at the 12th Scientific Meeting of the Classification and Data Analysis Group of the Italian Statistical Society (CLADAG 2019;
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: * An extensive appendix with R-package training material to encourage duplication and modification of the presented computations and research;
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