COMBINATORIAL MARKOV RANDOM FIELDS AND THEIR APPLICATIONS TO INFORMATION ORGANIZATION

We show that the definition of neighbor in Markov random fields as defined by Besag (1974) when the joint distribution of the sites is not positive is not well-defined. In a random field with finite number of sites we study the conditions under which giving the value at extra sites will change the belief of an agent about one site. Also the conditions under which the information from some sites...

Word clustering is a conventional and important NLP task, and the literature has suggested two kinds of approaches to this problem. One is based on the distributional similarity and the other relies on the co-occurrence of two words in lexicosyntactic patterns. Although the two methods have been discussed separately, it is promising to combine them since they are complementary with each other. ...

This paper presents a novel tissue proliferation model of extraneuronal filaceous material consisting of accumulation of the amyloid beta-proteins. Proposed model is constructed using a 3-D Gaussian Hidden Markov Random Field obtained from fluorescent microscopy measurements.

The problem we address in this paper is to label datapoints when the information about them is provided primarily in terms of their subsets or groups. The knowledge we have for a group is a numerical weight or likelihood value for each group member to belong to same class. These likelihood values are computed given a class specific model, either explicit or implicit, of the pattern we wish to l...

We present a new brain segmentation framework which we apply to T1-weighted magnetic resonance image segmentation. The innovation of the algorithm in comparison to the state-of-the-art of nonsupervised brain segmentation is twofold. First, the algorithm is entirely non-parametric and non-supervised. We can therefore enhance the classically used gray level information of the images by other feat...