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» Why Is Rule Learning Optimistic and How to Correct It
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AAAI
1998
13 years 9 months ago
Boosting in the Limit: Maximizing the Margin of Learned Ensembles
The "minimum margin" of an ensemble classifier on a given training set is, roughly speaking, the smallest vote it gives to any correct training label. Recent work has sh...
Adam J. Grove, Dale Schuurmans
ICMI
2007
Springer
112views Biometrics» more  ICMI 2007»
14 years 2 months ago
How to distinguish posed from spontaneous smiles using geometric features
Automatic distinction between posed and spontaneous expressions is an unsolved problem. Previously cognitive sciences’ studies indicated that the automatic separation of posed f...
Michel François Valstar, Hatice Gunes, Maja...
IJCNN
2000
IEEE
14 years 10 hour ago
Unsupervised Classification of Complex Clusters in Networks of Spiking Neurons
For unsupervised clustering in a network of spiking neurons we develop a temporal encoding of continuously valued data to obtain arbitrary clustering capacity and precision with a...
Sander M. Bohte, Johannes A. La Poutré, Joo...
RAID
1999
Springer
14 years 20 days ago
Combining Knowledge Discovery and Knowledge Engineering to Build IDSs
We have been developing a data mining (i.e., knowledge discovery) framework, MADAM ID, for Mining Audit Data for Automated Models for Intrusion Detection [LSM98, LSM99b, LSM99a]. ...
Wenke Lee, Salvatore J. Stolfo
VL
2010
IEEE
216views Visual Languages» more  VL 2010»
13 years 6 months ago
Explanatory Debugging: Supporting End-User Debugging of Machine-Learned Programs
Many machine-learning algorithms learn rules of behavior from individual end users, such as taskoriented desktop organizers and handwriting recognizers. These rules form a “prog...
Todd Kulesza, Simone Stumpf, Margaret M. Burnett, ...