Ensemble Methods: Foundations and Algorithms (Hardcover)
Zhi-Hua Zhou
- 出版商: CRC
- 出版日期: 2012-06-06
- 售價: $3,150
- 貴賓價: 9.5 折 $2,993
- 語言: 英文
- 頁數: 236
- 裝訂: Hardcover
- ISBN: 1439830037
- ISBN-13: 9781439830031
-
相關分類:
Algorithms-data-structures 資料結構與演算法
-
相關翻譯:
集成學習:基礎與算法 (簡中版)
立即出貨 (庫存 < 3)
買這商品的人也買了...
-
$990$891 -
$1,100$1,078 -
$5,270$5,007 -
$690$587 -
$2,980$2,831 -
$1,200$948 -
$780$616 -
$580$452 -
$1,130$961 -
$400$380 -
$480$408 -
$480$379 -
$360$306 -
$520$411 -
$590$443 -
$380$304 -
$780$616 -
$520$411 -
$590$443 -
$360$281 -
$780$616 -
$450$383 -
$450$356 -
$590$460 -
$390$371
商品描述
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.