Recurrent Predictive Models for Sequence Segmentation
published: Oct. 8, 2007, recorded: September 2007, views: 3419
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Many sequential data sets have a segmental structure, and similar types of segments occur repeatedly. We consider sequences where the underlying phenomenon of interest is governed by a small set of models that change over time. Potential examples of such data are environmental, genomic, and economic sequences. Given a target sequence and a (possibly multivariate) sequence of observation values, we consider the problem of finding a small collection of models that can be used to explain the target phenomenon in a piecewise fashion using the observation values. We assume the same model will be used for multiple segments. We give an algorithm for this task based on first segmenting the sequence using dynamic programming, and then using k-median or facility location techniques to find the optimal set of models. We report on some experimental results.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !