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基于混合正则化的无标签领域的归纳迁移学习
  • ISSN号：0023-074X
  • 期刊名称：科学通报
  • 时间：0
  • 页码：1618-1625
  • 语言：中文
  • 分类：TP18[自动化与计算机技术—控制科学与工程;自动化与计算机技术—控制理论与控制工程] TP391.41[自动化与计算机技术—计算机应用技术;自动化与计算机技术—计算机科学与技术]
  • 作者机构：[1]中国科学院计算技术研究所智能信息处理重点实验室,北京100190, [2]惠普中国实验室,北京100084, [3]中国科学院研究生院,北京100049
  • 相关基金：国家自然科学基金（批准号：60435010,60675010）、国家高技术研究发展计划（编号：2006AA012128,2007AA012132）、国家重点基础研究发展计划（编号：2007CB311004）和国家科技支撑计划（编号：2006BAc08806）资助项目
  • 相关项目：基于分类超曲面的覆盖分类算法与理论研究
作者： 何清|罗平|史忠植|庄福振|
关键词： 迁移学习, 归纳学习, 直推式学习, 混合正则化
中文摘要：

近年来迁移学习已经引起了越来越广泛的兴趣,签数据以及源领域数据是不同分布的分类问题,且建立一个归纳分类模型对新来的目标数据进行预测.首先分析了直推式迁移学习（transductive transfer learning）中存在的类别比例漂移问题,然后提出归一化的方法使得预测的类别比例接近于实际样本类别比例.更进一步,提出了一种基于混合正则化框架的归纳迁移学习算法.其中包括目标领域分布结构的流形正则化,预测概率的熵正则化,以及类别比例的期望正则化.这个框架被用于从源领域到目标领域学习的归纳模型中.最后,在实际文本数据集上的实验结果表明,提出的归纳迁移学习模型是有效的,同时该模型可以直接对新来的目标数据进行预测.

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期刊信息
  • 《科学通报》
  • 北大核心期刊（2011版）
  • 主管单位:中国科学院
  • 主办单位:中国科学院
  • 主编：周光召
  • 地址：北京东黄城根北街16号
  • 邮编：100717
  • 邮箱：csb@scichina.org
  • 电话：010-64036120 64012686
  • 国际标准刊号：ISSN：0023-074X
  • 国内统一刊号：ISSN：11-1784/N
  • 邮发代号:80-213
  • 获奖情况:
  • 首届国家期刊奖,中国期刊方阵“双高”期刊,第三届中国出版政府奖
  • 国内外数据库收录:
  • 美国化学文摘（网络版）,美国数学评论（网络版）,美国工程索引,日本日本科学技术振兴机构数据库,中国中国科技核心期刊,中国北大核心期刊（2004版）,中国北大核心期刊（2008版）,中国北大核心期刊（2011版）,中国北大核心期刊（2014版）,中国北大核心期刊（2000版）
  • 被引量:81792