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ICDM
2002
IEEE
105views Data Mining» more  ICDM 2002»
15 years 9 months ago
Empirical Comparison of Various Reinforcement Learning Strategies for Sequential Targeted Marketing
We empirically evaluate the performance of various reinforcement learning methods in applications to sequential targeted marketing. In particular, we propose and evaluate a progre...
Naoki Abe, Edwin P. D. Pednault, Haixun Wang, Bian...
ICDM
2002
IEEE
133views Data Mining» more  ICDM 2002»
15 years 9 months ago
Learning with Progressive Transductive Support Vector Machine
Support vector machine (SVM) is a new learning method developed in recent years based on the foundations of statistical learning theory. By taking a transductive approach instead ...
Yisong Chen, Guoping Wang, Shihai Dong
KDD
2009
ACM
172views Data Mining» more  KDD 2009»
15 years 9 months ago
Learning dynamic temporal graphs for oil-production equipment monitoring system
Learning temporal graph structures from time series data reveals important dependency relationships between current observations and histories. Most previous work focuses on learn...
Yan Liu, Jayant R. Kalagnanam, Oivind Johnsen
MCS
2001
Springer
15 years 9 months ago
Dependence among Codeword Bits Errors in ECOC Learning Machines: An Experimental Analysis
One of the main factors affecting the effectiveness of ECOC methods for classification is the dependence among the errors of the computed codeword bits. We present an extensive ...
Francesco Masulli, Giorgio Valentini
HRI
2007
ACM
15 years 8 months ago
Learning by demonstration with critique from a human teacher
Learning by demonstration can be a powerful and natural tool for developing robot control policies. That is, instead of tedious hand-coding, a robot may learn a control policy by ...
Brenna Argall, Brett Browning, Manuela M. Veloso