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2013 Fiscal Year Final Research Report

A study on optimizing graphs for machine learning algorithms

Research Project

  • PDF
Project/Area Number 24800036
Research Category

Grant-in-Aid for Research Activity Start-up

Allocation TypeSingle-year Grants
Research Field Intelligent informatics
Research InstitutionKyoto University

Principal Investigator

KARASUYAMA Masayuki  京都大学, 化学研究所, 助教 (40628640)

Project Period (FY) 2012-08-31 – 2014-03-31
Keywords機械学習 / グラフ / バイオインフォマティクス
Research Abstract

A variety types of network data have been attracted wide attention, such as protein interaction network in biology and link relationships in social networks. This research has studied statistical algorithms to analyze these network data, which can be represented as ``graph'', and developed highly accurate methods for prediction problem on graphs compared to existing approaches.

  • Research Products

    (4 results)

All 2013 2012

All Journal Article (1 results) (of which Peer Reviewed: 1 results) Presentation (3 results)

  • [Journal Article] Multiple Graph Label Propagation by Sparse Integration2013

    • Author(s)
      M Karasuyama, and H Mamitsuka
    • Journal Title

      IEEE Transactions on Neural Networks and Learning Systems

      Volume: vol.24, no.12 Pages: 1999—2012

    • DOI

      10.1109/TNNLS.2013.2271327

    • Peer Reviewed
  • [Presentation] Manifold-based Similarity Adaptation for Label Propagation2013

    • Author(s)
      M Karasuyama, and H Mamitsuka
    • Organizer
      Advances in Neural Information Processing Systems (NIPS)
    • Year and Date
      20130000
  • [Presentation] 局所線形近似に基づくラベル伝播のための類似度適合2013

    • Author(s)
      烏山昌幸, 馬見塚拓
    • Organizer
      情報論的学習理論と機械学習研究会 (IBISML)
    • Year and Date
      20130000
  • [Presentation] ラベル伝播アルゴリズムにおける複数グラフのスパース結合法2012

    • Author(s)
      烏山昌幸, 馬見塚拓
    • Organizer
      情報論的学習理論と機械学習研究会 (IBISML)
    • Year and Date
      20120000

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Published: 2015-07-16  

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