Travelled to:1 × Canada
1 × France
1 × Italy
1 × Slovenia
9 × USA
Collaborated with:H.Mamitsuka A.Nakamura B.Zadrozny A.C.Lozano H.Li Y.Liu J.Langford P.M.Long A.Arnold E.P.D.Pednault S.Rosset N.K.Verma C.Apté R.Schroko H.Li A.Niculescu-Mizil C.Perlich J.R.M.Hosking P.Melville C.Pendus C.K.Reddy D.L.Jensen V.P.Thomas J.J.Bennett G.F.Anderson B.R.Cooley M.Kowalczyk M.Domick T.Gardinier
Talks about:learn (11) use (6) model (5) reinforc (4) method (4) tempor (3) sensit (3) causal (3) optim (3) cost (3)
Person: Naoki Abe
DBLP: Abe:Naoki
Contributed to:
2010200920082007200620042002
200019991998199619951994Wrote 17 papers:
- KDD-2010-AbeMPRJTBACKDG #learning #optimisation #using
- Optimizing debt collections using constrained reinforcement learning (NA, PM, CP, CKR, DLJ, VPT, JJB, GFA, BRC, MK, MD, TG), pp. 75–84.
- KDD-2009-LozanoALR #modelling #visual notation
- Grouped graphical Granger modeling methods for temporal causal modeling (ACL, NA, YL, SR), pp. 577–586.
- KDD-2009-LozanoLNLPHA #modelling
- Spatial-temporal causal modeling for climate change attribution (ACL, HL, ANM, YL, CP, JRMH, NA), pp. 587–596.
- KDD-2008-LozanoA #multi
- Multi-class cost-sensitive boosting with p-norm loss functions (ACL, NA), pp. 506–514.
- KDD-2007-ArnoldLA #modelling #visual notation
- Temporal causal modeling with graphical granger methods (AA, YL, NA), pp. 66–75.
- KDD-2006-AbeZL #detection #learning
- Outlier detection by active learning (NA, BZ, JL), pp. 504–509.
- KDD-2004-AbeVAS #learning
- Cross channel optimized marketing by reinforcement learning (NA, NKV, CA, RS), pp. 767–772.
- KDD-2004-AbeZL #learning #multi
- An iterative method for multi-class cost-sensitive learning (NA, BZ, JL), pp. 3–11.
- KDD-2002-PednaultAZ #learning
- Sequential cost-sensitive decision making with reinforcement learning (EPDP, NA, BZ), pp. 259–268.
- ICML-2000-MamitsukaA #database #learning #mining #performance #query #scalability
- Efficient Mining from Large Databases by Query Learning (HM, NA), pp. 575–582.
- ICML-1999-AbeL #concept #learning #linear #probability #using
- Associative Reinforcement Learning using Linear Probabilistic Concepts (NA, PML), pp. 3–11.
- ICML-1999-AbeN #internet #learning
- Learning to Optimally Schedule Internet Banner Advertisements (NA, AN), pp. 12–21.
- ICML-1998-AbeM #learning #query #using
- Query Learning Strategies Using Boosting and Bagging (NA, HM), pp. 1–9.
- ICML-1998-NakamuraA #algorithm #collaboration #predict #using
- Collaborative Filtering Using Weighted Majority Prediction Algorithms (AN, NA), pp. 395–403.
- ICML-1996-AbeL #learning #modelling #using #word
- Learning Word Association Norms Using Tree Cut Pair Models (NA, HL), pp. 3–11.
- ICML-1995-AbeLN #2d #algorithm #learning #online #using
- On-line Learning of Binary Lexical Relations Using Two-dimensional Weighted Majority Algorithms (NA, HL, AN), pp. 3–11.
- ICML-1994-AbeM #predict #probability
- A New Method for Predicting Protein Secondary Structures Based on Stochastic Tree Grammars (NA, HM), pp. 3–11.