Clustering is traditionally viewed as an unsupervised method for data analysis. However, in some cases information about the problem domain is available in addition to the data in...
Kiri Wagstaff, Claire Cardie, Seth Rogers, Stefan ...
Abstract. In this paper we present a novel approach to combining multiple kernels where the kernels are computed from different information channels. In contrast to traditional me...
Fei Yan, Krystian Mikolajczyk, Josef Kittler, Muha...
The changes in the dynamics of the economy and the corresponding mobility and fluctuations of knowledge workers within organizations make continuous social learning an essential f...
Karin Schoefegger, Nicolas Weber, Stefanie N. Lind...
In this paper, we propose a new variant of Latent Dirichlet Allocation(LDA): Collective LDA (C-LDA), for multiple corpora modeling. C-LDA combines multiple corpora during learning...
We present a framework for efficient, accurate approximate Bayesian inference in generalized linear models (GLMs), based on the expectation propagation (EP) technique. The paramete...
Matthias Seeger, Sebastian Gerwinn, Matthias Bethg...