BibTeX
@MISC{_planningto,
author = {},
title = {Planning to learn: Recent Developments and Future Directions},
year = {}
}
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Abstract
The talk will cover my lab’s recent research concerning planning to learn and discuss its relationships to relevant work of other researchers. I will first introduce a machine-learning application that had motivated us to explore how knowledge-discovery workflows could be designed automatically using a data-mining ontology. In particular, we mined product-engineering data such as CAD documents for structural design patterns [1]. This task entailed the orchestration of numerous data-preprocessing and machine-learning algorithms in surprisingly complex workflows. The involved technique of sorted refinement [2] lead to non-linear, non-tree knowledge discovery workflows, in that the data flow was forked into individually processed data streams later reuniting as inputs to an inductive logic programming algorithm. Given the circumstances above, we wanted to see if the user could be alleviated from composing such complex workflows manually. To this end, we started to develop a knowledge-discovery ontology to capture the functionalities, constraints and mutual relations among data mining algorithms. Building on established
Keyphrases
future direction recent development complex workflow non-tree knowledge discovery machine-learning algorithm relevant work knowledge-discovery ontology structural design pattern lab recent research knowledge-discovery workflow sorted refinement data stream cad document machine-learning application data flow inductive logic involved technique data-mining ontology mutual relation
