Abstract
A versatile incremental learning algorithm is introduced for supervised neural network type classifiers. The proposed algorithm, called Learn++, exploits the synergistic expressive power of an ensemble of weak classifiers for learning additional information from new data. Learn++ is capable of learning new classes, without forgetting previously acquired knowledge, even when the previously used data is no longer available. Furthermore, Learn++ is independent of the specific type of the classifier, and adds the incremental learning capability to any supervised neural network classifier.
| Original language | English (US) |
|---|---|
| Pages | 1742-1747 |
| Number of pages | 6 |
| State | Published - 2002 |
| Event | 2002 International Joint Conference on Neural Networks (IJCNN '02) - Honolulu, HI, United States Duration: May 12 2002 → May 17 2002 |
Other
| Other | 2002 International Joint Conference on Neural Networks (IJCNN '02) |
|---|---|
| Country/Territory | United States |
| City | Honolulu, HI |
| Period | 5/12/02 → 5/17/02 |
All Science Journal Classification (ASJC) codes
- Software
- Artificial Intelligence
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