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» On Learning Decision Trees with Large Output Domains
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QSIC
2007
IEEE
14 years 2 months ago
Learning Effective Oracle Comparator Combinations for Web Applications
Web application testers need automated, effective approaches to validate the test results of complex, evolving web applications. In previous work, we developed a suite of automate...
Sara Sprenkle, Emily Hill, Lori L. Pollock
ICML
2000
IEEE
14 years 8 months ago
Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning
Algorithms for feature selection fall into two broad categories: wrappers that use the learning algorithm itself to evaluate the usefulness of features and filters that evaluate f...
Mark A. Hall
ECML
2007
Springer
14 years 1 months ago
Clustering Trees with Instance Level Constraints
Abstract. Constrained clustering investigates how to incorporate domain knowledge in the clustering process. The domain knowledge takes the form of constraints that must hold on th...
Jan Struyf, Saso Dzeroski
ICML
2010
IEEE
13 years 8 months ago
Detecting Large-Scale System Problems by Mining Console Logs
Surprisingly, console logs rarely help operators detect problems in large-scale datacenter services, for they often consist of the voluminous intermixing of messages from many sof...
Wei Xu, Ling Huang, Armando Fox, David Patterson, ...
COLT
1998
Springer
13 years 12 months ago
Improved Boosting Algorithms using Confidence-Rated Predictions
Abstract. We describe several improvements to Freund and Schapire's AdaBoost boosting algorithm, particularly in a setting in which hypotheses may assign confidences to each o...
Robert E. Schapire, Yoram Singer