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Development of Learning Theory based on Information Measure

Development of Learning Theory based on Information Measure
基于信息测量的学习理论的发展
批准号:
19300051
负责人:
TAKEUCHI Junichi
金额:
$11.9万
依托单位:
依托单位国家:
日本
项目类别:
Grant-in-Aid for Scientific Research (B)
财政年份:
2007
资助国家:
日本
项目状态:
已结题
起止时间:
2007 至 2010

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中文摘要
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英文摘要
We studied machine learning, information theory, and other related topics from a unified viewpoint of minimum description length principle (MDL principle). In particular, we obtained the new sight on the relation between geometrical structure of tree models and stochastic complexity (SC) and that between communication channel capacity and SC. We also studied ensemble learning and kernel method, and obtained efficient learning method for them. Further, on the basis of the fundamental knowledge obtained in this research, we proposed new learning based methods for incident detection in network security, universal portfolio, super resolution etc., and showed their efficiency.
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会议论文
マルコフモデルの幾何学について
关于马尔可夫模型的几何
DOI: --
发表时间: 2009
期刊:
影响因子: --
作者: [竹内純一]
通讯作者: 竹内純一
DOI: --
发表时间: 2010
期刊:
影响因子: --
作者: [川喜田雅則, 竹内純一]
通讯作者: 竹内純一
統計的モデリング/情報理論と学習理論
统计建模/信息论和学习理论
DOI: --
发表时间: 2008
期刊:
影响因子: --
作者: [Konishi, S. and Kitagawa, G., 小西貞則・越智義道・大森裕浩, 小西貞則・竹内純一]
通讯作者: 小西貞則・竹内純一
DOI: 10.1109/icacte.2008.48
发表时间: 2008-12
期刊: 2008 International Conference on Advanced Computer Theory and Engineering
影响因子: --
作者: [Jun Guo;Norikazu Takahashi;Wenxin Hu]
通讯作者: Jun Guo;Norikazu Takahashi;Wenxin Hu
43
    The Minimum Description Length Principle and Learning Theory
    • 批准号:
      24500018
    • 项目类别:
      Grant-in-Aid for Scientific Research (C)
    • 资助金额:
      $3.41万
    • 财政年份:
      2012
    • 负责人:
      TAKEUCHI Junichi
    • 依托单位:
    海外基金