Travelled to:1 × Canada
1 × China
1 × Israel
1 × Japan
1 × United Kingdom
2 × Australia
4 × USA
Collaborated with:Y.Tsuboi A.Inokuchi Y.Baba M.Sugiyama D.Kimura S.Hido T.Idé T.Koyanagi K.Tsuda I.Sato H.Nakagawa R.Tomioka T.Suzuki J.Hu B.K.Ray M.Singh K.Yamasaki H.Saigo N.Nori K.Yamashita H.Ikai Y.Imanaka T.Morimura H.Hachiya T.Tanaka Y.Nohara E.Kai P.P.Ghosh R.I.Maruf A.Ahmed M.Kuroda S.Inoue T.Hiramatsu M.Kimura S.Shimizu K.Kobayashi M.Blondel N.Ueda M.Kitsuregawa N.Nakashima
Talks about:kernel (4) learn (4) label (3) graph (3) base (3) algorithm (2) structur (2) sequenc (2) predict (2) comput (2)
Person: Hisashi Kashima
DBLP: Kashima:Hisashi
Contributed to:
2015
201420132012
2012201020082004200420032002Wrote 15 papers:
- KDD-2015-BabaKNKGIAKIHKS #low cost #predict
- Predictive Approaches for Low-Cost Preventive Medicine Program in Developing Countries (YB, HK, YN, EK, PPG, RIM, AA, MK, SI, TH, MK, SS, KK, KT, MS, MB, NU, MK, NN), pp. 1681–1690.
- KDD-2015-NoriKYII #modelling #multi #predict
- Simultaneous Modeling of Multiple Diseases for Mortality Prediction in Acute Hospital Care (NN, HK, KY, HI, YI), pp. 855–864.
- ICML-c2-2014-SatoKN #analysis #normalisation
- Latent Confusion Analysis by Normalized Gamma Construction (IS, HK, HN), pp. 1116–1124.
- KDD-2013-BabaK #crowdsourcing #estimation #quality #statistics
- Statistical quality estimation for general crowdsourcing tasks (YB, HK), pp. 554–562.
- ICML-2012-KimuraK #kernel #performance
- Fast Computation of Subpath Kernel for Trees (DK, HK), p. 81.
- ICPR-2012-HidoK #graph #learning #similarity
- Hash-based structural similarity for semi-supervised Learning on attribute graphs (SH, HK), pp. 3009–3012.
- ICML-2010-MorimuraSKHT #approximate #learning #parametricity
- Nonparametric Return Distribution Approximation for Reinforcement Learning (TM, MS, HK, HH, TT), pp. 799–806.
- ICML-2010-TomiokaSSK #algorithm #learning #matrix #performance #rank
- A Fast Augmented Lagrangian Algorithm for Learning Low-Rank Matrices (RT, TS, MS, HK), pp. 1087–1094.
- ICPR-2008-KashimaHRS #clustering #distance #using
- K-means clustering of proportional data using L1 distance (HK, JH, BKR, MS), pp. 1–4.
- ICPR-2008-KashimaYIS
- Regression with interval output values (HK, KY, AI, HS), pp. 1–4.
- ICPR-2008-TsuboiK #sequence
- A new objective function for sequence labeling (YT, HK), pp. 1–4.
- ICML-2004-KashimaT #algorithm #graph #kernel #learning #sequence
- Kernel-based discriminative learning algorithms for labeling sequences, trees, and graphs (HK, YT).
- KDD-2004-IdeK #detection
- Eigenspace-based anomaly detection in computer systems (TI, HK), pp. 440–449.
- ICML-2003-KashimaTI #graph #kernel
- Marginalized Kernels Between Labeled Graphs (HK, KT, AI), pp. 321–328.
- ICML-2002-KashimaK #kernel
- Kernels for Semi-Structured Data (HK, TK), pp. 291–298.