Name
Abstract | ||
|---|---|---|
We present a qualitative model-learning system, Qoph, developed for application to scientific discovery problems. Qophlearns the structuralrelations between a set of observed variables. It has been shown capable of learning models with intermediate (unmeasured) variables, and intermediate relations, under different levels of noise, and from qualitative or quantitative data. A biological application of Qophis explored. An additional significant outcome of this work is the discovery and identification of kernel subsets of key states that must be present for model-learning to succeed. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-73920-3_12 | Computational Discovery of Scientific Knowledge |
Keywords | Field | DocType |
intermediate relation,additional significant outcome,observed variable,different level,scientific discovery problem,key state,biological application,learning qualitative models,kernel subsets,qualitative model-learning system,biological systems,qophis explored | Inductive logic programming,Kernel (linear algebra),Scientific discovery,Computer science,Artificial intelligence,Learning models,Machine learning | Conference |
Citations | PageRank | References |
2 | 0.39 | 25 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Simon M. Garrett | 1 | 111 | 6.49 |
| G. M. Coghill | 2 | 200 | 23.24 |
| Ashwin Srinivasan | 3 | 1167 | 121.29 |
| Ross D. King | 4 | 1774 | 194.85 |