A Fast Active Learning Algorithm for Link Classification

author: Giovanni Zappella, Dipartimento di matematica "F. Enriques", Università Degli Studi Di Milano
published: Aug. 6, 2013,   recorded: April 2013,   views: 65
Categories

Slides

Related Open Educational Resources

Related content

Report a problem or upload files

If 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.
Lecture popularity: You need to login to cast your vote.
  Bibliography

Description

We present a very efficient active learning algorithm for link classification in signed networks. Our algorithm is motivated by a stochastic model in which edge labels are obtained through perturbations of an initial sign assignment consistent with a two-clustering of the nodes. We provide a theoretical analysis within this model, showing that we can achieve an optimal (to within a constant factor) number of mistakes on any graph G = (V;E) such that |E| = \Omega(|V|^3/2) by querying O(|V|^3/2) edge labels. More generally, we show an algorithm that achieves optimality to within a factor of O(k) by querying at most order of |V| + (|V|/k)^3/2 edge labels. The running time of this algorithm is at most of order |E| + |V| log |V|.

See Also:

Download slides icon Download slides: machine_zappella_learning_algorithm_01.pdf (255.8¬†KB)


Help icon Streaming Video Help

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

make sure you have javascript enabled or clear this field: