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Physical Sciences and Mathematics Commons

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University of Massachusetts - Amherst

Selected Works

2006

Articles 1 - 4 of 4

Full-Text Articles in Physical Sciences and Mathematics

Combinatorial Markov Random Fields, Ron Bekkerman, Erik Learned-Miller Jan 2006

Combinatorial Markov Random Fields, Ron Bekkerman, Erik Learned-Miller

Erik G Learned-Miller

A combinatorial random variable is a discrete random variable defined over a combinatorial set (e.g., a power set of a given set). In this paper we introduce combinatorial Markov random fields (Comrafs), which are Markov random fields where some of the nodes are combinatorial random variables. We argue that Comrafs are powerful models for unsupervised and semi-supervised learning. We put Comrafs in perspective by showing their relationship with several existing models. Since it can be problematic to apply existing inference techniques for graphical models to Comrafs, we design two simple and efficient inference algorithms specific for Comrafs, which are based …


Improved Generative Models For Continuous Image Features Through Tree-Structured Non-Parametric Distributions, Marwan Mattar, Erik Learned-Miller Jan 2006

Improved Generative Models For Continuous Image Features Through Tree-Structured Non-Parametric Distributions, Marwan Mattar, Erik Learned-Miller

Erik G Learned-Miller

Density estimation arises in a wide range of vision problems and methods which can deal with high dimensional image features are of great importance. While in principle a non- parametric distribution can be estimated for the full featu re distribution using Parzen win- dows technique, the amount of data to make these estimates accurate is usually either unattainable or unmanageable. Consequently, most modelers resort to parametric models such as mixtures of Gaussians (or other more complicated parametric forms) or make in- dependence assumptions about the features. Such assumptions could be detrimental to the performance of vision systems since realistically, image …


The Processing And Analysis Of In Situ Gene Expression Images Of The Mouse Brain, Manjunatha Jagalur1,, Chris Pal, Erik Learned-Miller, R Zoeller, David Kulp Jan 2006

The Processing And Analysis Of In Situ Gene Expression Images Of The Mouse Brain, Manjunatha Jagalur1,, Chris Pal, Erik Learned-Miller, R Zoeller, David Kulp

Erik G Learned-Miller

Many important high throughput projects use in situ gene expression detection technology and require the analysis of images of spatial cross sections of organisms taken at cellular level resolution. Projects creating gene expression atlases at unprecedented scales for the embryonic fruit fly as well as the embryonic and adult mouse already involve the analysis of hundreds of thousands of high resolution experimental images. We present an end-to-end approach for processing raw in situ expression imagery and performing subsequent analysis. We use a nonlinear image registration technique specifically adapted for mapping expression images to anatomical annotations and a method for extracting …


Minimal Test Collections For Retrieval Evaluation, Ben Carterette, James Allan, Ramesh Sitaraman Jan 2006

Minimal Test Collections For Retrieval Evaluation, Ben Carterette, James Allan, Ramesh Sitaraman

Ramesh Sitaraman

Accurate estimation of information retrieval evaluation metrics such as average precision require large sets of relevance judgments. Building sets large enough for evaluation of real-world implementations is at best inefficient, at worst infeasible. In this work we link evaluation with test collection construction to gain an understanding of the minimal judging effort that must be done to have high confidence in the outcome of an evaluation. A new way of looking at average precision leads to a natural algorithm for selecting documents to judge and allows us to estimate the degree of confidence by defining a distribution over possible document …