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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 DBLP: Tokuyama:Takeshi

Contributed to:

KDIR/KMIS 20132013
KDIR 20112011
CIKM 20102010
STOC 20062006
ICALP 20012001
STOC 20012001
VLDB 19981998
KDD 19971997
STOC 19971997
PODS 19961996
SIGMOD 19961996
VLDB 19961996

Wrote 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.

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