Name
Abstract | ||
|---|---|---|
The training of Emergent Self-organizing Maps (ESOM )w ith large datasets can be a computationally demanding task. Batch learning may be used to speed up training. It is demonstrated here, however, that the representation of clusters in the data space on maps trained with batch learning is poor compared to sequential training. This effect occurs even for very clear cluster structures. The k-batch learning algorithm is preferrable, because it creates the same quality of representation as sequential learning but maintains important properties of batch learning that can be exploited for speedup. |
Year | Venue | Field |
|---|---|---|
2006 | ESANN | Competitive learning,Online machine learning,Multi-task learning,Stability (learning theory),Semi-supervised learning,Instance-based learning,Active learning (machine learning),Computer science,Algorithm,Unsupervised learning,Artificial intelligence,Machine learning |
DocType | Citations | PageRank |
Conference | 4 | 0.41 |
References | Authors | |
4 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mario Nöcker | 1 | 42 | 2.53 |
| Fabian Mörchen | 2 | 372 | 17.94 |
| Alfred Ultsch | 3 | 403 | 51.77 |