Statistical Classification and Cluster Processes
published: July 30, 2009, recorded: June 2009, views: 4791
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.
After an introduction to the notion of an exchangeable random partition, we continue with a more detailed discussion of the Ewens process and some of its antecedents. The concept of an exchangeable cluster process will be described, the main example being the Gauss-Ewens process. Some applications of cluster processes will be discussed, including problems of classification or supervised learning, and cluster analysis (unsupervised learning). A second type of probabilistic model based on point processes is also described. By contrast, which the Gauss-Ewes cluster process, the domain associated with each class is more diffuse and not localized in the feature space. For both models, the classification problem is interpreted as the problem of computing the predictive distribution for the class of a new object having a given feature vector. In one case, this is a conditional distribution given the observed features, in the other a Papangelou conditional intensity.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !