Weizsäcker, Lorenz ; Fürnkranz, Johannes (2009)
Margin Driven Separate and Conquer by Working Set Expansion.
Report, Bibliographie
Kurzbeschreibung (Abstract)
Covering algorithms for binary classification build a list of one-sided partial models in a greedy manner. The original motivation therefor stems from the context of rule learning where the expressiveness of a single rule is too limited to serve as standalone model. If the model space is richer, the decomposition into subproblems is not strictly necessary but separately solved subproblems might still lead to better models specially when the subproblems are less demanding in terms of the input model. We investigate in this direction with an AQR style covering algorithm that uses an SVM base learner for discovering the subproblems along with a corresponding output model. The experimental study covers different criteria for the selection of the subproblems and as well as several vector kernels of varying model capacity.
| Typ des Eintrags: | Report |
|---|---|
| Erschienen: | 2009 |
| Autor(en): | Weizsäcker, Lorenz ; Fürnkranz, Johannes |
| Art des Eintrags: | Bibliographie |
| Titel: | Margin Driven Separate and Conquer by Working Set Expansion |
| Sprache: | Englisch |
| Publikationsjahr: | 2009 |
| URL / URN: | http://www.ke.informatik.tu-darmstadt.de/publications/report... |
| Kurzbeschreibung (Abstract): | Covering algorithms for binary classification build a list of one-sided partial models in a greedy manner. The original motivation therefor stems from the context of rule learning where the expressiveness of a single rule is too limited to serve as standalone model. If the model space is richer, the decomposition into subproblems is not strictly necessary but separately solved subproblems might still lead to better models specially when the subproblems are less demanding in terms of the input model. We investigate in this direction with an AQR style covering algorithm that uses an SVM base learner for discovering the subproblems along with a corresponding output model. The experimental study covers different criteria for the selection of the subproblems and as well as several vector kernels of varying model capacity. |
| ID-Nummer: | TUD-KE-2009-06 |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik > Knowledge Engineering 20 Fachbereich Informatik |
| Hinterlegungsdatum: | 24 Jun 2011 14:42 |
| Letzte Änderung: | 05 Mär 2013 09:49 |
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