We have developed a machine learning toolbox, called SHOGUN, which is designed for unified large-scale learning for a broad range of feature types and learning settings. It offers...
We show how a preselection of hidden variables can be used to efficiently train generative models with binary hidden variables. The approach is based on Expectation Maximization (...
To save memory and improve speed, vectorial data such as images and signals are often represented as strings of discrete symbols (i.e., sketches). Chariker (2002) proposed a fast ...
Yasuo Tabei, Takeaki Uno, Masashi Sugiyama, Koji T...
We study the forward and backward substitution phases of a sparse multifrontal factorization. These phases are often neglected in papers on sparse direct factorization but, in man...
Patrick Amestoy, Iain S. Duff, Abdou Guermouche, T...
This paper suggests a framework for mining subjectively interesting pattern sets that is based on two components: (1) the encoding of prior information in a model for the data min...
Tijl De Bie, Kleanthis-Nikolaos Kontonasios, Eirin...