Planning to Learn with a Knowledge Discovery Ontology
published: Dec. 1, 2008, recorded: October 2008, views: 4790
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.
Assembly of optimized knowledge discovery workfows requires awareness of and extensive knowledge about the principles and mutual relations between diverse data processing and mining algorithms.We aim at alleviating this burden by automatically proposing workfows for the given type of inputs and required outputs of the discovery process. The methodology adopted in this study is to define a formal conceptualization of knowledge types and data mining algorithms and design a planning algorithm, which extracts constraints from this conceptualization for the given user's input-output requirements. We demonstrate our approach in two use cases, one from scientific discovery in genomics and another from advanced engineering.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !