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HICSS
2003
IEEE
116views Biometrics» more  HICSS 2003»
14 years 4 months ago
Modeling Instrumental Conditioning - The Behavioral Regulation Approach
Basically, instrumental conditioning is learning through consequences: Behavior that produces positive results (high “instrumental response”) is reinforced, and that which pro...
Jose J. Gonzalez, Agata Sawicka
SAC
2010
ACM
13 years 8 months ago
A machine-checked soundness proof for an efficient verification condition generator
Verification conditions (VCs) are logical formulae whose validity implies the correctness of a program with respect to a specification. The technique of checking software properti...
Frédéric Vogels, Bart Jacobs 0002, F...
KDD
2009
ACM
178views Data Mining» more  KDD 2009»
14 years 11 months ago
Constrained optimization for validation-guided conditional random field learning
Conditional random fields(CRFs) are a class of undirected graphical models which have been widely used for classifying and labeling sequence data. The training of CRFs is typicall...
Minmin Chen, Yixin Chen, Michael R. Brent, Aaron E...
SIGMOD
2009
ACM
140views Database» more  SIGMOD 2009»
14 years 11 months ago
Estimating the confidence of conditional functional dependencies
Conditional functional dependencies (CFDs) have recently been proposed as extensions of classical functional dependencies that apply to a certain subset of the relation, as specif...
Graham Cormode, Lukasz Golab, Flip Korn, Andrew Mc...
DIS
2009
Springer
14 years 5 months ago
MICCLLR: Multiple-Instance Learning Using Class Conditional Log Likelihood Ratio
Multiple-instance learning (MIL) is a generalization of the supervised learning problem where each training observation is a labeled bag of unlabeled instances. Several supervised ...
Yasser El-Manzalawy, Vasant Honavar