| Autor: |
Vikas C. Raykar, Matthias Wolf, Marcos Salganicoff, Sarang Lakare, Murat Dundar |
| Rok vydání: |
2008 |
| Předmět: |
|
| Zdroj: |
ICML |
| DOI: |
10.1145/1390156.1390193 |
| Popis: |
In this study we introduce a novel algorithm for learning a polyhedron to describe the target class. The proposed approach takes advantage of the limited subclass information made available for the negative samples and jointly optimizes multiple hyperplane classifiers each of which is designed to classify positive samples from a subclass of the negative samples. The flat faces of the polyhedron provides robustness whereas multiple faces contributes to the flexibility required to deal with complex datasets. Apart from improving the prediction accuracy of the system, the proposed polyhedral classifier also provides run-time speedups as a by-product when executed in a cascaded framework in real-time. We evaluate the performance of the proposed technique on a real-world Colon dataset both in terms of prediction accuracy and online execution speed. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
|
