We propose a spectral learning approach to shape segmentation. The method is composed of a constrained spectral clustering algorithm that is used to supervise the segmentation of a...
Abstract. This paper presents a probabilistic model for combining cluster ensembles utilizing information theoretic measures. Starting from a co-association matrix which summarizes...
We study the interaction between global and local techniques in data mining. Specifically, we study the collections of frequent sets in clusters produced by a probabilistic clust...
In this paper we describe an interdisciplinary collaboration between researchers in machine learning and oceanography. The collaboration was formed to study the problem of open oc...
Joshua M. Lewis, Pincelli M. Hull, Kilian Q. Weinb...
For systematic analyses of quantitative mass spectrometry data a method was developed in order to reveal peptides within a protein, that show differences in comparison with the rem...