公开(公告)号：US20090281981A1

公开(公告)日：2009-11-12

申请号：US12436667

申请日：2009-05-06

申请人： Barry Y. Chen ,  William G. Hanley ,  Tracy D. Lemmond ,  Lawrence J. Hiller ,  David A. Knapp ,  Marshall J. Mugge

发明人： Barry Y. Chen ,  William G. Hanley ,  Tracy D. Lemmond ,  Lawrence J. Hiller ,  David A. Knapp ,  Marshall J. Mugge

IPC分类号： G06N7/02

CPC分类号： G06K9/6282 ,  G06N99/005

摘要： A hybrid machine learning methodology and system for classification that combines classical random forest (RF) methodology with discriminant analysis (DA) techniques to provide enhanced classification capability. A DA technique which uses feature measurements of an object to predict its class membership, such as linear discriminant analysis (LDA) or Andersen-Bahadur linear discriminant technique (AB), is used to split the data at each node in each of its classification trees to train and grow the trees and the forest. When training is finished, a set of n DA-based decision trees of a discriminant forest is produced for use in predicting the classification of new samples of unknown class.

摘要翻译： 混合机器学习方法和分类系统，将经典随机森林（RF）方法与判别分析（DA）技术相结合，提供增强的分类能力。 使用使用对象的特征测量来预测其类成员资格（例如线性判别分析（LDA）或者安徒生 - 巴多尔线性判别技术（AB））的DA技术来分割每个分类树中的每个节点上的数据 培育和种植树木和森林。 当训练完成时，产生一组基于判别式森林的基于DA的决策树，用于预测未知类别的新样本的分类。

公开(公告)号：US08306942B2

公开(公告)日：2012-11-06

申请号：US12436667

申请日：2009-05-06

申请人： Barry Y. Chen ,  William G. Hanley ,  Tracy D. Lemmond ,  Lawrence J. Hiller ,  David A. Knapp ,  Marshall J. Mugge

发明人： Barry Y. Chen ,  William G. Hanley ,  Tracy D. Lemmond ,  Lawrence J. Hiller ,  David A. Knapp ,  Marshall J. Mugge

IPC分类号： G06F15/00 ,  G06F15/18

CPC分类号： G06K9/6282 ,  G06N99/005

摘要： A hybrid machine learning methodology and system for classification that combines classical random forest (RF) methodology with discriminant analysis (DA) techniques to provide enhanced classification capability. A DA technique which uses feature measurements of an object to predict its class membership, such as linear discriminant analysis (LDA) or Andersen-Bahadur linear discriminant technique (AB), is used to split the data at each node in each of its classification trees to train and grow the trees and the forest. When training is finished, a set of n DA-based decision trees of a discriminant forest is produced for use in predicting the classification of new samples of unknown class.

摘要翻译： 混合机器学习方法和分类系统，将经典随机森林（RF）方法与判别分析（DA）技术相结合，提供增强的分类能力。 使用使用对象的特征测量来预测其类成员资格（例如线性判别分析（LDA）或者安徒生 - 巴多尔线性判别技术（AB））的DA技术来分割每个分类树中的每个节点上的数据 培育和种植树木和森林。 当训练完成时，产生一组基于判别式森林的基于DA的决策树，用于预测未知类别的新样本的分类。