Name
Abstract | ||
|---|---|---|
We propose an episodic memory-based approach to the problem of pattern capture and recognition. We show how a generic episodic memory module can be enhanced with an incremental retrieval al- gorithm that can deal with the kind of data avail- able for this application. We evaluate this approach on a goal schema recognition task on a complex and noisy dataset. The memory module was able to achieve the same level of performance as statistical approaches and doing so in a scalable manner. |
Year | Venue | Field |
|---|---|---|
2006 | AAAI Fall Symposium: Capturing and Using Patterns for Evidence Detection | Episodic memory,Computer science,Artificial intelligence,Machine learning |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
10 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| dan g tecuci | 1 | 131 | 12.84 |
| Bruce Porter | 2 | 316 | 30.66 |