Quasi-Incremental Bayesian Classifier

author: Estevam R. Hruschka, Federal University of Săo Carlos
published: Jan. 29, 2008,   recorded: September 2007,   views: 141

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

This talk describes and empirically evaluates a Quasi-Incremental Bayesian Classifier (QBC) designed to be used when a classification task must be performed in dynamic systems such as sensor networks, which are continuously receiving new piece of information to be stored in huge databases. Therefore, the knowledge that needs to be extracted from these databases is continuously evolving and the learning process may need to go on almost indefinitely. The induction proposed by QBC is performed in two steps; in the first one a traditional Bayesian Network (BN) induction algorithm is performed using an initial amount of data. As far as new data is available, only the numerical parameters of the classifier are updated. The conducted experiments showed that QBC tends to maintain the average correct classification rates obtained with non-incremental classifiers while decreasing the time needed to induce the classifier.

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: