Travelled to:1 × Italy
8 × USA
Collaborated with:N.Abe I.Takigawa S.Zhu ∅ M.Shiga R.Wan K.F.Aoki X.Zheng H.Ding S.Zhang K.Hashimoto K.F.Aoki-Kinoshita N.Ueda M.Kanehisa
Talks about:probabilist (2) similar (2) predict (2) multipl (2) cluster (2) effici (2) queri (2) model (2) learn (2) field (2)
Person: Hiroshi Mamitsuka
DBLP: Mamitsuka:Hiroshi
Contributed to:
2013
200720072006
2005
2003200019981994Wrote 9 papers:
- KDD-2013-ZhengDMZ #collaboration #interactive #matrix #multi #predict
- Collaborative matrix factorization with multiple similarities for predicting drug-target interactions (XZ, HD, HM, SZ), pp. 1025–1033.
- ECIR-2007-ZhuTZM #clustering #documentation #multi #probability
- A Probabilistic Model for Clustering Text Documents with Multiple Fields (SZ, IT, SZ, HM), pp. 331–342.
- KDD-2007-ShigaTM #approach #clustering #composition #network
- A spectral clustering approach to optimally combining numericalvectors with a modular network (MS, IT, HM), pp. 647–656.
- KDD-2006-HashimotoAUKM #mining #order #performance #probability
- A new efficient probabilistic model for mining labeled ordered trees (KH, KFAK, NU, MK, HM), pp. 177–186.
- SAC-2005-WanMA #array #markov #random #similarity #using
- Cleaning microarray expression data using Markov random fields based on profile similarity (RW, HM, KFA), pp. 206–207.
- ICML-2003-Mamitsuka #analysis
- Hierarchical Latent Knowledge Analysis for Co-occurrence Data (HM), pp. 504–511.
- ICML-2000-MamitsukaA #database #learning #mining #performance #query #scalability
- Efficient Mining from Large Databases by Query Learning (HM, NA), pp. 575–582.
- ICML-1998-AbeM #learning #query #using
- Query Learning Strategies Using Boosting and Bagging (NA, HM), pp. 1–9.
- ICML-1994-AbeM #predict #probability
- A New Method for Predicting Protein Secondary Structures Based on Stochastic Tree Grammars (NA, HM), pp. 3–11.