Approximate Bayesian Gaussian process (GP) classification techniques are powerful nonparametric learning methods, similar in appearance and performance to support vector machines....
We study the problem of finding the dominant eigenvector of the sample covariance matrix, under additional constraints on the vector: a cardinality constraint limits the number of...
One of the most popular student modeling techniques currently available is Constraint Based Modeling (CBM), which is based on Ohlsson's theory of learning from performance err...
Biological shape modeling is an essential task that is required for systems biology efforts to simulate complex cell behaviors. Statistical learning methods have been used to buil...
Tao Peng, Wei Wang, Gustavo K. Rohde, Robert F. Mu...
Inference methods for detecting attacks on information resources typically use signature analysis or statistical anomaly detection methods. The former have the advantage of attack...