A Random Subspace Method for Co-Training

WANG Jiao; LUO Si-wei; ZENG Xian-hua

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A Random Subspace Method for Co-Training

更新时间：2025-07-16

- A Random Subspace Method for Co-Training
- Acta Electronica Sinica Vol. 36, Issue S1, Pages: 60-65(2008)
- 作者机构：
  
  北京交通大学计算机与信息技术学院,北京,100044
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP181
- Published：2008
- 稿件说明：
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WANG Jiao, LUO Si-wei, ZENG Xian-hua. A Random Subspace Method for Co-Training[J]. Acta Electronica Sinica, 2008, 36(S1): 60-65.
DOI：

WANG Jiao, LUO Si-wei, ZENG Xian-hua. A Random Subspace Method for Co-Training[J]. Acta Electronica Sinica, 2008, 36(S1): 60-65. DOI：

摘要

半监督学习是近年来的一个研究热点.协同训练(co-training)是利用未标记数据来提高传统监督学习性能的一种半监督学习范式.本文提出一种基于随机子空间的协同训练算法(RAndom Subspace CO-training

简称为RAS-CO).该算法探讨多视图的协同训练.用随机判别的理论分析了算法的分类精度和泛化能力.讨论了随机子空间的维数和个数对分类性能的影响.在UCI数据集上的实验结果表明

与其它同类算法相比

RASCO算法有较好的性能.

Abstract

Semi-supervised learning has received much attention recently.Co-training is a kind of semi-supervised learning method which uses unlabeled data to boost the performance of standard supervised learning algorithms.A novel co-training style algorithm

RASCO(for RAndom Subspace CO-training)

is proposed which uses stochastic discrimination theory to extend co-training to multi-view situation.The accuracy and generalizability of RASCO are analyzed.The influences of the parameters of RASCO are discussed.Experiments on UCI data set demonstrate that RASCO is more effective than other co-training style algorithms.

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