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KDD
1994
ACM
140views Data Mining» more  KDD 1994»
15 years 7 months ago
A Comparison of Pruning Methods for Relational Concept Learning
Pre-Pruning and Post-Pruning are two standard methods of dealing with noise in concept learning. Pre-Pruning methods are very efficient, while Post-Pruning methods typically are m...
Johannes Fürnkranz
PKDD
2000
Springer
108views Data Mining» more  PKDD 2000»
15 years 6 months ago
Application of Reinforcement Learning to Electrical Power System Closed-Loop Emergency Control
This paper investigates the use of reinforcement learning in electric power system emergency control. The approach consists of using numerical simulations together with on-policy M...
Christophe Druet, Damien Ernst, Louis Wehenkel
SDM
2007
SIAM
133views Data Mining» more  SDM 2007»
15 years 4 months ago
Change-Point Detection using Krylov Subspace Learning
We propose an efficient algorithm for principal component analysis (PCA) that is applicable when only the inner product with a given vector is needed. We show that Krylov subspace...
Tsuyoshi Idé, Koji Tsuda
SIGMOD
2004
ACM
209views Database» more  SIGMOD 2004»
16 years 3 months ago
MAIDS: Mining Alarming Incidents from Data Streams
Real-time surveillance systems, network and telecommunication systems, and other dynamic processes often generate tremendous (potentially infinite) volume of stream data. Effectiv...
Y. Dora Cai, David Clutter, Greg Pape, Jiawei Han,...
137
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SDM
2004
SIAM
141views Data Mining» more  SDM 2004»
15 years 4 months ago
Active Mining of Data Streams
Most previously proposed mining methods on data streams make an unrealistic assumption that "labelled" data stream is readily available and can be mined at anytime. Howe...
Wei Fan, Yi-an Huang, Haixun Wang, Philip S. Yu