Ensemble Methods

This book PDF is perfect for those who love Business & Economics genre, written by Zhi-Hua Zhou and published by CRC Press which was released on 06 June 2012 with total hardcover pages 238. You could read this book directly on your devices with pdf, epub and kindle format, check detail and related Ensemble Methods books below.

Ensemble Methods
Ensemble Methods
Author : Zhi-Hua Zhou
File Size : 46,7 Mb
Publisher : CRC Press
Language : English
Release Date : 06 June 2012
ISBN : 9781439830031
Pages : 238 pages
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Ensemble Methods by Zhi-Hua Zhou Book PDF Summary

An up-to-date, self-contained introduction to a state-of-the-art machine learning approach, Ensemble Methods: Foundations and Algorithms shows how these accurate methods are used in real-world tasks. It gives you the necessary groundwork to carry out further research in this evolving field. After presenting background and terminology, the book covers the main algorithms and theories, including Boosting, Bagging, Random Forest, averaging and voting schemes, the Stacking method, mixture of experts, and diversity measures. It also discusses multiclass extension, noise tolerance, error-ambiguity and bias-variance decompositions, and recent progress in information theoretic diversity. Moving on to more advanced topics, the author explains how to achieve better performance through ensemble pruning and how to generate better clustering results by combining multiple clusterings. In addition, he describes developments of ensemble methods in semi-supervised learning, active learning, cost-sensitive learning, class-imbalance learning, and comprehensibility enhancement.

Ensemble Methods

Ensemble Methods by Zhi-Hua Zhou

An up-to-date, self-contained introduction to a state-of-the-art machine learning approach, Ensemble Methods: Foundations and Algorithms shows how these accurate methods are used in real-world tasks. It gives you the necessary groundwork to carry out further research in this evolving field. After presenting background and terminology, the book covers the main

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Ensemble Methods for Machine Learning

Ensemble Methods for Machine Learning by Gautam Kunapuli

Ensemble machine learning combines the power of multiple machine learning approaches, working together to deliver models that are highly performant and highly accurate. Inside Ensemble Methods for Machine Learning you will find: Methods for classification, regression, and recommendations Sophisticated off-the-shelf ensemble implementations Random forests, boosting, and gradient boosting Feature engineering

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Ensemble Machine Learning

Ensemble Machine Learning by Cha Zhang,Yunqian Ma

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

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Ensemble Machine Learning

Ensemble Machine Learning by Cha Zhang,Yunqian Ma

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

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Ensemble Machine Learning Cookbook

Ensemble Machine Learning Cookbook by Dipayan Sarkar,Vijayalakshmi Natarajan

Implement machine learning algorithms to build ensemble models using Keras, H2O, Scikit-Learn, Pandas and more Key FeaturesApply popular machine learning algorithms using a recipe-based approachImplement boosting, bagging, and stacking ensemble methods to improve machine learning modelsDiscover real-world ensemble applications and encounter complex challenges in Kaggle competitionsBook Description Ensemble modeling

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Ensemble Methods in Data Mining

Ensemble Methods in Data Mining by Giovanni Seni,John Elder

Ensemble methods have been called the most influential development in Data Mining and Machine Learning in the past decade. They combine multiple models into one usually more accurate than the best of its components. Ensembles can provide a critical boost to industrial challenges -- from investment timing to drug discovery,

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Ensemble Learning Algorithms With Python

Ensemble Learning Algorithms With Python by Jason Brownlee

Predictive performance is the most important concern on many classification and regression problems. Ensemble learning algorithms combine the predictions from multiple models and are designed to perform better than any contributing ensemble member. Using clear explanations, standard Python libraries, and step-by-step tutorial lessons, you will discover how to confidently and

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Hands On Ensemble Learning with R

Hands On Ensemble Learning with R by Prabhanjan Narayanachar Tattar

Explore powerful R packages to create predictive models using ensemble methods Key Features Implement machine learning algorithms to build ensemble-efficient models Explore powerful R packages to create predictive models using ensemble methods Learn to build ensemble models on large datasets using a practical approach Book Description Ensemble techniques are used

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