Travelled to:1 × Slovenia
2 × Canada
7 × USA
Collaborated with:S.J.Stolfo G.Tandon M.V.Mahoney ∅ W.Fan J.Zhang A.L.Prodromidis S.Tselepis W.Lee D.W.Fan
Talks about:learn (11) meta (4) partit (3) detect (3) cost (3) distribut (2) network (2) databas (2) model (2) data (2)
Person: Philip K. Chan
DBLP: Chan:Philip_K=
Contributed to:
20072002
199919981997199619951995
19931989Wrote 10 papers:
- KDD-2007-TandonC #detection #network #validation
- Weighting versus pruning in rule validation for detecting network and host anomalies (GT, PKC), pp. 697–706.
- KDD-2002-MahoneyC #detection #learning #modelling #network #novel
- Learning nonstationary models of normal network traffic for detecting novel attacks (MVM, PKC), pp. 376–385.
- ICML-1999-FanSZC #classification #named
- AdaCost: Misclassification Cost-Sensitive Boosting (WF, SJS, JZ, PKC), pp. 97–105.
- KDD-1998-ChanS #case study #detection #learning #scalability #towards
- Toward Scalable Learning with Non-Uniform Class and Cost Distributions: A Case Study in Credit Card Fraud Detection (PKC, SJS), pp. 164–168.
- KDD-1997-StolfoPTLFC #database #distributed #java #named
- JAM: Java Agents for Meta-Learning over Distributed Databases (SJS, ALP, ST, WL, DWF, PKC), pp. 74–81.
- KDD-1996-ChanS #database #modelling
- Sharing Learned Models among Remote Database Partitions by Local Meta-Learning (PKC, SJS), pp. 2–7.
- ICML-1995-ChanS #comparative #evaluation
- A Comparative Evaluation of Voting and Meta-learning on Partitioned Data (PKC, SJS), pp. 90–98.
- KDD-1995-ChanS #machine learning #scalability
- Learning Arbiter and Combiner Trees from Partitioned Data for Scaling Machine Learning (PKC, SJS), pp. 39–44.
- CIKM-1993-ChanS #learning #multi
- Experiments on Multi-Strategy Learning by Meta-Learning (PKC, SJS), pp. 314–323.
- ML-1989-Chan #induction #learning
- Inductive Learning with BCT (PKC), pp. 104–108.