Name
Abstract | ||
|---|---|---|
Machine learning research has been very successful at producing powerful, broadly- applicable classification learners. However, many practical learning problems do not fit the classification framework well, and as a re- sult the initial phase of suitably formulating the problem and incorporating the relevant domain knowledge can be very difficult and time-consuming. Here we propose a frame- work to systematize and speed this process, based on the notion of version space alge- bra. We extend the notion of version spaces beyond concept learning, and propose that carefully-tailored version spaces for complex applications can be built by composing sim- pler, restricted version spaces. We illustrate our approach with SMARTedit, a program- ming by demonstration application for repet- itive text-editing that uses version space alge- bra to guide a search over text-editing action sequences. We demonstrate the system on a suite of repetitive text-editing problems and present experimental results showing its ef- fectiveness in learning from a small number of examples. |
Year | Venue | Keywords |
|---|---|---|
2000 | ICML | version space algebra,domain knowledge,machine learning,concept learning |
Field | DocType | ISBN |
Programming by demonstration,Robot learning,Algorithmic learning theory,Instance-based learning,Active learning (machine learning),Computer science,Theoretical computer science,Hyper-heuristic,Artificial intelligence,Computational learning theory,Algebra,Inductive programming,Machine learning | Conference | 1-55860-707-2 |
Citations | PageRank | References |
34 | 3.48 | 10 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tessa A. Lau | 1 | 909 | 56.12 |
| Pedro Domingos | 2 | 13226 | 1199.48 |
| Daniel S. Weld | 3 | 10298 | 1127.49 |