Pattern Analysis over Graphs, and Bioinformatics Applications
published: Dec. 3, 2009, recorded: October 2009, views: 5247
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.
1. Classification and regression over graphs. Overview: positive definite graph kernels based on walk, subtrees etc.., as well as other non p.d. similarity functions (eg from graph matching) that can be used to compare graphs and do classification/regression with kernel methods. Applications: QSAR in chemistry, image classification
2. Detecting patterns in the context of regression or classification with a graph as prior knowledge over the features. Overview: in a classical regression/classification problem over high-dimensional vectors. Control the complexity, by using priors that can be derived from the graph over the vectors, and how they can be used as penalty functions for classification and regression. This will cover diffusion kernels and other kernels over graphs, fused lasso, structured group lasso. Application in bioinformatics.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !