Corrine Cheng

Learn More
Mining informative patterns from very large, dynamically changing databases poses numerous interesting challenges. Data summarizations (e.g., data bubbles) have been proposed to compress very large static databases into representative points suitable for subsequent effective hierarchical cluster analysis. In many real world applications, however, the(More)
Greiner and Zhou [1] presented ELR, a discriminative parameter-learning algorithm that maximizes conditional likelihood (CL) for a fixed Bayesian Belief Network (BN) structure, and demonstrated that it often produces classifiers that are more accurate than the ones produced using the generative approach (OFE), which finds maximal likelihood parameters. This(More)
  • 1