Abstract
In this paper we continue our previous research on kernel subset analysis for Qualitative Model Learning (QML).We focus on investigating the kernel subsets and learning precision
of QML when the number of the training data is relatively large, which makes the corresponding kernel subset experiments very computationally expensive to perform. We use a twocompartment
model with two qualitatively different inputs as our testbed to exhaustively perform the kernel subset experiments by the GENMODEL algorithm. An analysis on the obtained
experimental results indicates that there exist patterns in the formation of kernel subsets, and the solution space analysis further reveals the distribution of kernel subsets in the solution
space.
of QML when the number of the training data is relatively large, which makes the corresponding kernel subset experiments very computationally expensive to perform. We use a twocompartment
model with two qualitatively different inputs as our testbed to exhaustively perform the kernel subset experiments by the GENMODEL algorithm. An analysis on the obtained
experimental results indicates that there exist patterns in the formation of kernel subsets, and the solution space analysis further reveals the distribution of kernel subsets in the solution
space.
| Original language | English |
|---|---|
| Title of host publication | Proceeding of the 12th UK Workshop on Computational Intelligence |
| Editors | P. De Wilde, G.M. Coghill, A.V. Kononova |
| Place of Publication | Edinburgh, UK |
| Publisher | IEEE Explore |
| Pages | 1-7 |
| Number of pages | 7 |
| ISBN (Electronic) | 978-1-4673-4392-3 |
| DOIs | |
| Publication status | Published - 2012 |
| Event | Computational Intelligence (UKCI), 2012 12th UK Workshop on - , United Kingdom Duration: 5 Sept 2012 → 7 Sept 2012 |
Conference
| Conference | Computational Intelligence (UKCI), 2012 12th UK Workshop on |
|---|---|
| Country/Territory | United Kingdom |
| Period | 5/09/12 → 7/09/12 |