Travelled to:1 × France
1 × India
1 × Portugal
2 × Greece
3 × Canada
4 × USA
Collaborated with:Y.Morimoto T.Fukuda S.Morishita C.Nguyen T.Asano H.V.Le ∅ K.Yoda J.Matousek K.Sadakane N.Takki-Chebihi N.Kaothanthong X.H.Phan N.Katoh H.Tamaki T.P.Nhung J.Chun H.Matsuzawa
Talks about:optim (6) rule (6) associ (5) algorithm (3) numer (3) multi (3) annot (3) mine (3) imag (3) dimension (2)
Person: Takeshi Tokuyama
DBLP: Tokuyama:Takeshi
Contributed to:
20132011
2010
2006
20012001
1998
19971997
1996
19961996Wrote 13 papers:
- KDIR-KMIS-2013-NhungNCLT #approach #image #learning #multi
- A Multiple Instance Learning Approach to Image Annotation with Saliency Map (TPN, CTN, JC, HVL, TT), pp. 152–159.
- KDIR-2011-NguyenLT #classification #image #multi
- Cascade of Multi-level Multi-instance Classifiers for Image Annotation (CTN, HVL, TT), pp. 14–23.
- CIKM-2010-NguyenKPT #image #topic
- A feature-word-topic model for image annotation (CTN, NK, XHP, TT), pp. 1481–1484.
- STOC-2006-AsanoMT #distance
- The distance trisector curve (TA, JM, TT), pp. 336–343.
- ICALP-2001-SadakaneTT #algorithm #combinator #sequence
- Combinatorics and Algorithms on Low-Discrepancy Roundings of a Real Sequence (KS, NTC, TT), pp. 166–177.
- STOC-2001-Tokuyama #multi #optimisation #parametricity #problem
- Minimax parametric optimization problems and multi-dimensional parametric searching (TT), pp. 75–83.
- VLDB-1998-MorimotoFMTY #algorithm #category theory #database #mining
- Algorithms for Mining Association Rules for Binary Segmentations of Huge Categorical Databases (YM, TF, HM, TT, KY), pp. 380–391.
- KDD-1997-YodaFMMT
- Computing Optimized Rectilinear Regions for Association Rules (KY, TF, YM, SM, TT), pp. 96–103.
- STOC-1997-AsanoKTT #approximate #polynomial #towards
- Covering Points in the Plane by k-Tours: Towards a Polynomial Time Approximation Scheme for General k (TA, NK, HT, TT), pp. 275–283.
- PODS-1996-FukudaMMT #mining
- Mining Optimized Association Rules for Numeric Attributes (TF, YM, SM, TT), pp. 182–191.
- SIGMOD-1996-FukudaMMT #2d #algorithm #data mining #mining #using #visualisation
- Data Mining Using Two-Dimensional Optimized Accociation Rules: Scheme, Algorithms, and Visualization (TF, YM, SM, TT), pp. 13–23.
- SIGMOD-1996-FukudaMMT96a #named
- SONAR: System for Optimized Numeric AssociationRules (TF, YM, SM, TT), p. 553.
- VLDB-1996-FukudaMMT #performance #using
- Constructing Efficient Decision Trees by Using Optimized Numeric Association Rules (TF, YM, SM, TT), pp. 146–155.