Semi-Supervised Laplacian Regularization of Kernel Canonical Correlation Analysis (2008)
Blaschko, M.B., Lampert, C.H., Gretton, A., Daelemans, W., Goethals, B., Morik, K.
Kernel canonical correlation analysis (KCCA) is a dimensionality reduction technique for paired data. By finding directions that maximize correlation, KCCA learns representations that are more...
Distribution-free Learning of Bayesian Network Structure (2008)
Sun, X., Daelemans, W., Goethals, B., Morik, K.
We present an independence-based method for learning Bayesian network (BN) structure without making any assumptions on the probability distribution of the domain. This is mainly useful for continuous...
Assessing Nonlinear Granger Causality from Multivariate Time Series (2008)
Sun, X., Daelemans, W., Goethals, B., Morik, K.
A straightforward nonlinear extension of Grangers concept of causality in the kernel framework is suggested. The kernel-based approach to assessing nonlinear Granger causality in multivariate time...