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have been developed in the authors' study of tree methods. Classification and Regression Trees reflects these two sides, covering the use;
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have been developed in the authors' study of tree methods. Classification and Regression Trees reflects these two sides, covering the use;
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Vergelijkbare producten zoals DATA MINING and MACHINE LEARNING. CLASSIFICATION PREDICTIVE TECHNIQUES: SUPPORT VECTOR MACHINE, LOGISTIC REGRESSION, DISCRIMINANT ANALYSIS and DECISION TREES
(regression), and reducing dimensionality, among other topics. This pocket reference includes sections that cover: Classification, using the;
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Provides a foundation in classical parametric methods of regression and classification essential for pursuing advanced topics in predictive;
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Classification and Regression is the first book to bring together, in a consistent statistical framework, the ideas of nonlinear modelling and Bayesian;
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The focus of this dissertation is on robust regression and classification in genetic association studies. In the context of robust;
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Classification and regression trees (CART) is one of the several contemporary statistical techniques with good promise for research in many;
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an overview of classification and regression. The book then introduces and examines various tested and proven visualization techniques for;
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An essential guide to two burgeoning topics in machine learning - classification trees and ensemble learning Ensemble Classification;
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presents you with a solid understanding on the predictors using classification tree mining and logistic regression analysis.Classification is the;
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This text provides a modern introduction to regression and classification with an emphasis on big data and R. Each chapter is partitioned;
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classification and regression trees. Chapters usually start with several useful case examples, describing the structure of typical datasets and proposing;
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classification and regression trees. Chapters usually start with several useful case examples, describing the structure of typical datasets and proposing;
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regression and Bayesian network classifiers), meta-classifiers, multi-dimensional classifiers and feature subset selection methods. Other parts;
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regression and classification concepts to real-world problems Perform predictive analysis using decision trees and random forests Carry out;
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and become adept at building powerful models for prediction and classification. Highlighting both underlying concepts and practical;
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This text provides a modern introduction to regression and classification with an emphasis on big data and R. Each chapter is partitioned;
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, multivariate analysis, CHAID, and classification and regression trees. Other chapters deal with perceptual positioning maps-point and vector, value;
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This book explores a proactive and domain-driven method to classification tasks. This novel proactive approach to data mining not only;
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regression modelling and then move into more advanced topics such as neural networks and tree-based methods. Finally, you'll delve into the;
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markets products. The author describes not only standard rating and scoring methods like Classification Trees or Logistic Regression, but also;
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of support vector machines (SVMs)-classification problems and regression problems. The book emphasizes the close connection between optimization;
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trees and the unsupervised random trees. When there are sufficient amount of labeled data available, supervised random trees provides a fast;
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how to choose machine learning algorithms for clustering, classification, and regression and know which is best suited for your problem What;
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and the methods used. Topics include: regression, logistic regression, classification and regression trees, neural networks, model cross;
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developments, Classification Methods for Remotely Sensed Data, Second Edition provides a comprehensive and up-to-date review of the entire field;
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