Topics In Random Forests
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Topics In Random Forests
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Author : Chao Chen
language : en
Publisher:
Release Date : 2005
Topics In Random Forests written by Chao Chen and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with categories.
Random Forests With R
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Author : Robin Genuer
language : en
Publisher: Springer Nature
Release Date : 2020-09-10
Random Forests With R written by Robin Genuer and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-09-10 with Mathematics categories.
This book offers an application-oriented guide to random forests: a statistical learning method extensively used in many fields of application, thanks to its excellent predictive performance, but also to its flexibility, which places few restrictions on the nature of the data used. Indeed, random forests can be adapted to both supervised classification problems and regression problems. In addition, they allow us to consider qualitative and quantitative explanatory variables together, without pre-processing. Moreover, they can be used to process standard data for which the number of observations is higher than the number of variables, while also performing very well in the high dimensional case, where the number of variables is quite large in comparison to the number of observations. Consequently, they are now among the preferred methods in the toolbox of statisticians and data scientists. The book is primarily intended for students in academic fields such as statistical education, but also for practitioners in statistics and machine learning. A scientific undergraduate degree is quite sufficient to take full advantage of the concepts, methods, and tools discussed. In terms of computer science skills, little background knowledge is required, though an introduction to the R language is recommended. Random forests are part of the family of tree-based methods; accordingly, after an introductory chapter, Chapter 2 presents CART trees. The next three chapters are devoted to random forests. They focus on their presentation (Chapter 3), on the variable importance tool (Chapter 4), and on the variable selection problem (Chapter 5), respectively. After discussing the concepts and methods, we illustrate their implementation on a running example. Then, various complements are provided before examining additional examples. Throughout the book, each result is given together with the code (in R) that can be used to reproduce it. Thus, the book offers readers essential information and concepts, together with examples and the software tools needed to analyse data using random forests.
Handbook Of Random Forests Theory And Applications For Remote Sensing
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Author : Ronny Hansch
language : en
Publisher: World Scientific Publishing Company
Release Date : 2024
Handbook Of Random Forests Theory And Applications For Remote Sensing written by Ronny Hansch and has been published by World Scientific Publishing Company this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024 with Computers categories.
This compendium provides a hands-on description of random forests. The text starts with a consistent introduction of general methods to create, train, and fuse ensembles of decision trees. Instead of limiting the explanation to the general-purpose layout of traditional random forests, this book outlines specifications during tree creation and training, that are especially well suited to analyze structured data such as images. The theoretical foundations are explained as deeply as practical and implementation issues. The many possible variations of the underlying Random Forest model are discussed as well as their implications on the outcome in order to provide insights into the influence of these parameters and their possible side-effects. Last but not least, this unique title provides specific examples of the usage of Random Forests for analysis tasks of remote sensing imagery.
Machine Learning
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Author : Christian Critelli
language : en
Publisher:
Release Date : 2021-03-03
Machine Learning written by Christian Critelli and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-03 with categories.
If you want to learn how decision trees and random forests work, plus create your own, this Machine Learning Algorithms visual book is for you. The topics covered in this Machine Learning Algorithms book are: - An overview of decision trees and random forests - A manual example of how a human would classify a dataset, compared to how a decision tree would work - How a decision tree works, and why it is prone to overfitting - How decision trees get combined to form a random forest - How to use that random forest to classify data and make predictions - How to determine how many trees to use in a random forest - Just where does the "randomness" come from - Out of Bag Errors & Cross-Validation - how good of a fit did the machine learning algorithm make? - Gini Criteria & Entropy Criteria - how to tell which split on a decision tree is best among many possible choices - And More
Decision Forests For Computer Vision And Medical Image Analysis
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Author : Antonio Criminisi
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-01-30
Decision Forests For Computer Vision And Medical Image Analysis written by Antonio Criminisi and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-01-30 with Computers categories.
This practical and easy-to-follow text explores the theoretical underpinnings of decision forests, organizing the vast existing literature on the field within a new, general-purpose forest model. Topics and features: with a foreword by Prof. Y. Amit and Prof. D. Geman, recounting their participation in the development of decision forests; introduces a flexible decision forest model, capable of addressing a large and diverse set of image and video analysis tasks; investigates both the theoretical foundations and the practical implementation of decision forests; discusses the use of decision forests for such tasks as classification, regression, density estimation, manifold learning, active learning and semi-supervised classification; includes exercises and experiments throughout the text, with solutions, slides, demo videos and other supplementary material provided at an associated website; provides a free, user-friendly software library, enabling the reader to experiment with forests in a hands-on manner.
Machine Learning For Beginners Book
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Author : Casimira Youngberg
language : en
Publisher: Independently Published
Release Date : 2021-07-09
Machine Learning For Beginners Book written by Casimira Youngberg and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-07-09 with categories.
Machine learning is the study of computer algorithms that improve automatically through experience and by the use of data. It is seen as a part of artificial intelligence. Machine learning algorithms build a model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as in medicine, email filtering, speech recognition, and computer vision, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks. If you are someone who learns by playing with the code and editing the data or equations to see what changes, then use those resources along with the book for a deeper understanding. The topics covered in this book are: -An overview of decision trees and random forests -A manual example of how a human would classify a dataset, compared to how a decision tree would work -How a decision tree works, and why it is prone to overfitting -How decision trees get combined to form a random forest -How to use that random forest to classify data and make predictions -How to determine how many trees to use in a random forest -Just where does the "randomness" come from -Out of Bag Errors & Cross-Validation - how good of a fit did the machine learning algorithm make? -Gini Criteria & Entropy Criteria - how to tell which split on a decision tree is best among many possible choices -And More
Random Forests
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Author : Yu. L. Pavlov
language : en
Publisher: Walter de Gruyter GmbH & Co KG
Release Date : 2019-01-14
Random Forests written by Yu. L. Pavlov and has been published by Walter de Gruyter GmbH & Co KG this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-01-14 with Mathematics categories.
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Ensemble Machine Learning
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Author : Cha Zhang
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-02-17
Ensemble Machine Learning written by Cha Zhang and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-02-17 with Computers categories.
It is common wisdom that gathering a variety of views and inputs improves the process of decision making, and, indeed, underpins a democratic society. Dubbed “ensemble learning” by researchers in computational intelligence and machine learning, it is known to improve a decision system’s robustness and accuracy. Now, fresh developments are allowing researchers to unleash the power of ensemble learning in an increasing range of real-world applications. Ensemble learning algorithms such as “boosting” and “random forest” facilitate solutions to key computational issues such as face recognition and are now being applied in areas as diverse as object tracking and bioinformatics. Responding to a shortage of literature dedicated to the topic, this volume offers comprehensive coverage of state-of-the-art ensemble learning techniques, including the random forest skeleton tracking algorithm in the Xbox Kinect sensor, which bypasses the need for game controllers. At once a solid theoretical study and a practical guide, the volume is a windfall for researchers and practitioners alike.
Statistical Issues In Machine Learning
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Author : Carolin Strobl
language : en
Publisher: Cuvillier Verlag
Release Date : 2008
Statistical Issues In Machine Learning written by Carolin Strobl and has been published by Cuvillier Verlag this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with categories.
Analyzing Random Forests
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Author : Choongsoon Bae
language : en
Publisher:
Release Date : 2008
Analyzing Random Forests written by Choongsoon Bae and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with categories.