Travelled to:1 × Italy
5 × USA
Collaborated with:∅ J.C.Schlimmer L.J.Eshelman S.Gallant G.Piatetsky-Shapiro L.Tan S.Dara C.Mayeux T.Xia L.Guo S.Wang
Talks about:learn (5) reinforc (2) sensit (2) agent (2) rank (2) mine (2) data (2) cost (2) use (2) represent (1)
Person: Ming Tan
DBLP: Tan:Ming
Contributed to:
2015
20132001
1993199119891988Wrote 7 papers:
- ICSE-v2-2015-TanTDM #fault #online #predict
- Online Defect Prediction for Imbalanced Data (MT, LT, SD, CM), pp. 99–108.
- KDD-2013-TanXGW #learning #metric #modelling #optimisation #rank #ranking
- Direct optimization of ranking measures for learning to rank models (MT, TX, LG, SW), pp. 856–864.
- KDD-T-2001-GallantPT #data mining #mining #web
- Value-based data mining and web mining for CRM (SG, GPS, MT), pp. 325–390.
- ICML-1993-Tan #independence #learning #multi
- Multi-Agent Reinforcement Learning: Independent versus Cooperative Agents (MT), pp. 330–337.
- ML-1991-Tan #learning #representation
- Learning a Cost-Sensitive Internal Representation for Reinforcement Learning (MT), pp. 358–362.
- ML-1989-TanS #approach #concept #learning #recognition
- Cost-Sensitive Concept Learning of Sensor Use in Approach ad Recognition (MT, JCS), pp. 392–395.
- ML-1988-TanE #classification #network #using
- Using Weighted Networks to Represent Classification Knowledge in Noisy Domains (MT, LJE), pp. 121–134.