|
| << vorige | volgende >> |
Tijdschrift beschrijving
Alle jaargangen van het bijbehorende tijdschrift
Alle afleveringen van het bijbehorende jaargang
Alle artikelen van de bijbehorende aflevering
Details van artikel 4 van 6 gevonden artikelen
| Titel: |
Learning from ambiguously labeled examples |
| Auteur: |
Hüllermeier, Eyke Beringer, Jürgen |
| Verschenen in: |
Intelligent data analysis |
| Paginering: | Jaargang 10 (2006) nr. 5 pagina's 419-439 |
| Jaar: |
2006-09-29 |
| Inhoud: |
Inducing a classification function from a set of examples in the form of labeled instances is a standard problem in supervised machine learning. In this paper, we are concerned with ambiguous label classification (ALC), an extension of this setting in which several candidate labels may be assigned to a single example. By extending three concrete classification methods to the ALC setting (nearest neighbor classification, decision tree learning, and rule induction) and evaluating their performance on benchmark data sets, we show that appropriately designed learning algorithms can successfully exploit the information contained in ambiguously labeled examples. Our results indicate that the fundamental idea of the extended methods, namely to disambiguate the label information by means of the inductive bias underlying (heuristic) machine learning methods, works well in practice. |
| Uitgever: |
IOS Press |
| Bronbestand: |
Elektronische Wetenschappelijke Tijdschriften |
Details van artikel 4 van 6 gevonden artikelen
| << vorige | volgende >> |