Name
Abstract | ||
|---|---|---|
We propose a mixed structure to form cascades for AdaBoost classifiers, where parallel strong classifiers are trained for each layer. The structure allows for rapid training and guarantees high hit rates without changing the original threshold. We implemented and tested the approach for two datasets from UCI [1], and compared results of binary classifiers using three different structures: standard AdaBoost, a cascade classifier with threshold adjustments, and the proposed structure. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1145/1363686.1364109 | SAC |
Keywords | Field | DocType |
binary classifier,guarantees high hit rate,standard adaboost,original threshold,threshold adjustment,different structure,adaboost classifier,cascade classifier,proposed structure,mixed structure,empirical evaluation,new structure,adaboost,image classification,machine learning | AdaBoost,Boosting methods for object categorization,Pattern recognition,Computer science,Cascading classifiers,Artificial intelligence,Contextual image classification,Machine learning,Binary number | Conference |
Citations | PageRank | References |
3 | 0.43 | 3 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| A. L. C. Barczak | 1 | 3 | 0.43 |
| M. J. Johnson | 2 | 3 | 0.43 |
| C. H. Messom | 3 | 35 | 6.04 |