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Details for article 4 of 12 found articles
| Title: |
Boosting Classifiers Built from Different Subsets of Features |
| Author: |
Janodet, ean-Christophe Sebban, Marc Suchier, Henri-Maxime |
| Appeared in: |
Fundamenta informaticae |
| Paging: | Volume 96 (2009) nr. 1-2 pages 89-109 |
| Year: |
2009-12-07 |
| Contents: |
We focus on the adaptation of boosting to representation spaces composed of different subsets of features. Rather than imposing a single weak learner to handle data that could come from different sources (e.g., images and texts and sounds), we suggest the decomposition of the learning task into several dependent sub-problems of boosting, treated by different weak learners, that will optimally collaborate during the weight update stage. To achieve this task, we introduce a new weighting scheme for which we provide theoretical results. Experiments are carried out and show that ourmethod works significantly better than any combination of independent boosting procedures. |
| Publisher: |
IOS Press |
| Source file: |
Elektronische Wetenschappelijke Tijdschriften |
Details for article 4 of 12 found articles
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