1.1 基本概念 机器学习、模式识别和数据挖掘的一个主要任务是从数据集构建良好的模型。“数据集”通常由特征矢量组成,其中每个特征矢量是通过使用一组特征对对象的描述。例如,请查看合成的Gaussian混合数据集,…
前言: 集合方法是一种最先进的学习方法,它训练多个基学习器,然后将它们结合起来使用,以boosting和bagging为代表。众所周知集成学习通常比单个学习器更准确,并且集成的方法在许多现实世界任务中已经取得了巨大…
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Klaus NordhausenSchool of Information Sciences FI-33014 University of TampereJohn Wiley & Sons, Ltd.International Statistical ReviewK. Nordhausen, "Ensemble methods: Foundations and algorithms by zhi-hua zhou," International Statistical Review, vol. 81, no. 3, pp. 470-470, 2013....
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. Aft...
装帧:精装 开本:其他 纸张:其他 分类:外文古旧书>英文书>计算机与互联网 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 necessar...
Ensemble methods that train multiple learners and then combine them to use, with Boosting and Bagging as representatives, are well-known machine learning approaches. It has become common sense that an ensemble is usually significantly more accurate than a single learner, and ensemble methods have alr...
Book Review: Ensemble Methods: Foundations and Algorithms. An up-to-date, self-contained introduction to a state-of-the-art machine learning approach, Ensemble Methods: Foundations and Algorithms shows how these ac... DVD Poel 被引量: 0发表: 0年 Stochastic Local Search: Foundations and Applicat...
Ensemble Methods: Foundations and Algorithms An up-to-date, self-contained introduction to a state-of-the-art machine learning approach, Ensemble Methods: Foundations and Algorithms shows how these ac... ZH Zhou - Ensemble Methods: Foundations and Algorithms 被引量: 0发表: 2012年 SWEPT VOLUMES:...
Consensus methods; Mixture-of-experts Ensemble methods are defined as “learning algorithms that construct a set of classifiers and then classify new data points by taking a (weighted) vote of their predictions” (Dietterich 2000). Assuming reasonable performance and diversity on the part of each ...