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Ensemble Classification Methods with Applications in R

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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Hands-On Ensemble Learning with R

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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Outlier Ensembles

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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Ensemble Learning

This updated compendium provides a methodical introduction with a coherent and unified repository of ensemble methods, theories, trends;

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Combining Pattern Classifiers

of classifier ensemble methods. Thoroughly updated, with MATLAB(R) code and practice data sets throughout, Combining Pattern Classifiers includes;

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Statistical Postprocessing of Ensemble Forecasts

Friederichs, Wahl, and Buschow (Chapter 5). The second section concludes with a discussion of forecast verification methods devised specifically for;

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Ensemble Machine Learning Cookbook

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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Multiple Fuzzy Classification Systems

and business applications. Fuzzy classifiers use fuzzy rules and do not require assumptions common to statistical classification. Rough set;

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Machine Learning

classification methods with a single, running example, highlighting similarities and differences between methods Presents R source code which shows how;

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Ensembles in Machine Learning Applications

(SUEMA) that was held in conjunction with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery;

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Data Classification

Comprehensive Coverage of the Entire Area of ClassificationResearch on the problem of classification tends to be fragmented across such;

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Data Classification

concludes with insight on variations of the classification process. It discusses ensembles, rare-class learning, distance function learning, active;

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Classification Methods for Internet Applications

This book explores internet applications in which a crucial role is played by classification, such as spam filtering, recommender systems;

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Classification Methods for Internet Applications

This book explores internet applications in which a crucial role is played by classification, such as spam filtering, recommender systems;

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Student Study Guide With IBM (R) SPSS (R) Workbook for Research Methods, Statistics, and Applications 2e

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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An Introduction to Bootstrap Methods with Applications to R

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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Handbook of Neural Computation

, 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

Data Mining for Business Analytics: Concepts, Techniques, and Applications with JMP Pro(R) presents an applied and interactive approach to;

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Breath Analysis for Medical Applications

This book describes breath signal processing technologies and their applications in medical sample classification and diagnosis. First, it;

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Applied Multivariate Statistics with R

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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Machine Learning and Knowledge Discovery in Databases

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)

Nonlinear Optimization in Electrical Engineering with Applications in MATLAB (R) provides an introductory course on nonlinear optimization;

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Time Series Clustering and Classification

clustering and classifying different types of large datasets, including time series data. Time Series Clustering and Classification includes relevant;

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Time Series Clustering and Classification

clustering and classifying different types of large datasets, including time series data. Time Series Clustering and Classification includes;

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Data Science

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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Statistical Learning and Modeling in Data Analysis

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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Multivariate Nonparametric Regression And Visualization

: * An extensive appendix with R-package training material to encourage duplication and modification of the presented computations and research;

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