Name
Abstract | ||
|---|---|---|
In pattern classification it is usually assumed that a training set of patterns along with their class labels is available. Multiple-Instance Learning (MIL) generalizes this problem setting by making weaker assumptions about the labeling information. We propose to generalize Support Vector Machines to take into account such weak labeling of the type found in MIL. Our method is able to identify superior discriminant functions, as is demonstrated in experiments on synthetic and image datasets. |
Year | Venue | Keywords |
|---|---|---|
2002 | AAAI/IAAI | generalized support vector machine,multiple instance,www,html,support vector,expert systems,support vector machine |
Field | DocType | ISBN |
Structured support vector machine,Training set,Instance-based learning,Active learning (machine learning),Computer science,Support vector machine,Image retrieval,Artificial intelligence,Relevance vector machine,Computational learning theory,Machine learning | Conference | 0-262-51129-0 |
Citations | PageRank | References |
47 | 2.28 | 9 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| cagatay demiralp | 1 | 795 | 36.82 |
| Thomas Hofmann | 2 | 10064 | 1001.83 |
| Ioannis Tsochantaridis | 3 | 2861 | 155.43 |