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KDD
2003
ACM
148views Data Mining» more  KDD 2003»
16 years 3 months ago
Mining concept-drifting data streams using ensemble classifiers
Recently, mining data streams with concept drifts for actionable insights has become an important and challenging task for a wide range of applications including credit card fraud...
Haixun Wang, Wei Fan, Philip S. Yu, Jiawei Han
DSS
2007
127views more  DSS 2007»
15 years 3 months ago
Large-scale regulatory network analysis from microarray data: modified Bayesian network learning and association rule mining
We present two algorithms for learning large-scale gene regulatory networks from microarray data: a modified informationtheory-based Bayesian network algorithm and a modified asso...
Zan Huang, Jiexun Li, Hua Su, George S. Watts, Hsi...
ICDM
2008
IEEE
112views Data Mining» more  ICDM 2008»
15 years 9 months ago
Supervised Inductive Learning with Lotka-Volterra Derived Models
We present a classification algorithm built on our adaptation of the Generalized Lotka-Volterra model, well-known in mathematical ecology. The training algorithm itself consists ...
Karen Hovsepian, Peter Anselmo, Subhasish Mazumdar
122
Voted
CVPR
2007
IEEE
16 years 5 months ago
From Videos to Verbs: Mining Videos for Activities using a Cascade of Dynamical Systems
Clustering video sequences in order to infer and extract activities from a single video stream is an extremely important problem and has significant potential in video indexing, s...
Pavan K. Turaga, Ashok Veeraraghavan, Rama Chellap...
120
Voted
DIS
2004
Springer
15 years 8 months ago
Mining Noisy Data Streams via a Discriminative Model
The two main challenges typically associated with mining data streams are concept drift and data contamination. To address these challenges, we seek learning techniques and models ...
Fang Chu, Yizhou Wang, Carlo Zaniolo