Abstract Recently, researchers have demonstrated that "loopy belief propagation" -the use of Pearl's polytree algorithm in a Bayesian network with loops -can perform well in the context of error-correcting codes. The most dramatic instance of this is the near Shannon

A multiple classifier system is a powerful solution to difficult pattern recognition problems involving large class sets and noisy input because it allows simultaneous use of arbitrary feature descriptors and classification procedures. Decisions by the classifiers can be represented as rankings

In this paper we present some experiments on the use of a probabilistic model to tag English text, i.e. to assign to each word the correct tag (part of speech) in the context of the sentence. The main novelty of these experiments is the use of untagged text in the training of the model. We have

, and also corrects for the correlations between neighboring voxels. This makes the same approach suitable for the spatial normalization of both high-quality magnetic resonance images, and low-resolution noisy positron emission tomography images. A fast algorithm has been developed that utilizes Taylor’s

information. Solely clustering videos on these tags is error-prone since the tags are typically noisy. To address these problems, we develop a structured model that considers the interaction between visual features, video tags and video clusters. We model tags from visual features, and correct noisy tags

-localization). In addition to de novo predictions, it can integrate often noisy, experimental interaction datasets. We observe that at given levels of sensitivity our predictions are more accurate than the existing highthroughput experimental datasets. We validate our predictions with new TAP-tagging experiments. Our

Almost all problems in computer vision are related in one form or another to the problem of estimating parameters from noisy data. In this tutorial, we present what is probably the most commonly used techniques for parameter estimation. These include linear least-squares (pseudo-inverse and eigen

The noisy channel model has been applied to a wide range of problems, including spelling correction. These models consist of two components: a source model and a channel model. Very little research has gone into improving the channel model for spelling correction. This paper describes a new

of TBIR. To address this challenge, we study the problem of tag completion where the goal is to automatically fill in the missing tags as well as correct noisy tags for given images. We represent the image-tag relation by a tag matrix, and search for the optimal tag matrix consistent with both

simple correction scheme is provided to reduce the bias. The noise characteristics in phase images are also studied and shown to be very different from those of the magnitude images. Common to both, however, is that the noise distributions are nearly Gaussian for SNR larger than two.