Material Detail

Learning a Distance Metric for Structured Network Prediction

Learning a Distance Metric for Structured Network Prediction

This video was recorded at NIPS Workshop on Learning to Compare Examples, Whistler 2006. Man-made or naturally-formed networks typically exhibit a high degree of structural regularity. In this paper, we introduce the problem of structured network prediction: given a set of n entities and a desired distribution for connectivity, return a likely set of edges connecting the entities together in a network having the specified degree distribution. Prediction is useful for initializing a network, augmenting an existing network, and for filtering existing networks, when the structure of the network is known. In order to capture the inter-dependencies amongst pairwise predictions to learn parameters of our model, we build upon recent structured output models. Novel in our approach is the use of... Show More
Rate

Quality

  • User Rating
  • Comments
  • Learning Exercises
  • Bookmark Collections
  • Course ePortfolios
  • Accessibility Info

More about this material

Comments

Log in to participate in the discussions or sign up if you are not already a MERLOT member.
hidden