Multi-class AdaBoost
Trevor Hastie, Saharon Rosset, Ji Zhu, Hui Zou
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Citation edge evidence
- A survey on ensemble learning→Multi-class AdaBoostLegacy edge — source detail unavailable
- Multi-class AdaBoost→Random ForestsLegacy edge — source detail unavailable
- Multi-class AdaBoost→Greedy function approximation: A gradient boosting machine.Legacy edge — source detail unavailable
- Multi-class AdaBoost→Classification and Regression Trees.Legacy edge — source detail unavailable
- Multi-class AdaBoost→Bagging PredictorsLegacy edge — source detail unavailable
- Multi-class AdaBoost→UCI Repository of machine learning databasesLegacy edge — source detail unavailable
- Multi-class AdaBoost→Improved boosting algorithms using confidence-rated predictionsLegacy edge — source detail unavailable
- Multi-class AdaBoost→Boosting the margin: a new explanation for the effectiveness of voting methodsLegacy edge — source detail unavailable
- Multi-class AdaBoost→UCI Repository of Machine Learning DatabasesLegacy edge — source detail unavailable
- Greedy function approximation: A gradient boosting machine.→Improved boosting algorithms using confidence-rated predictionsLegacy edge — source detail unavailable
- Greedy function approximation: A gradient boosting machine.→Classification and Regression Trees.Legacy edge — source detail unavailable
- Boosting the margin: a new explanation for the effectiveness of voting methods→Improved boosting algorithms using confidence-rated predictionsLegacy edge — source detail unavailable
- Boosting the margin: a new explanation for the effectiveness of voting methods→UCI Repository of machine learning databasesLegacy edge — source detail unavailable
- Boosting the margin: a new explanation for the effectiveness of voting methods→Bagging PredictorsLegacy edge — source detail unavailable
- Boosting the margin: a new explanation for the effectiveness of voting methods→UCI Repository of Machine Learning DatabasesLegacy edge — source detail unavailable
- Improved boosting algorithms using confidence-rated predictions→Boosting the margin: a new explanation for the effectiveness of voting methodsLegacy edge — source detail unavailable
- Improved boosting algorithms using confidence-rated predictions→UCI Repository of Machine Learning DatabasesLegacy edge — source detail unavailable
- Random Forests→Boosting the margin: a new explanation for the effectiveness of voting methodsLegacy edge — source detail unavailable
- Random Forests→Bagging PredictorsLegacy edge — source detail unavailable
- A survey on ensemble learning→Random ForestsLegacy edge — source detail unavailable
- A survey on ensemble learning→Bagging PredictorsLegacy edge — source detail unavailable
What this paper cites, inside the corpus
| Paper | Year | Cited |
|---|---|---|
| Random Forests | 2001 | 131,109 |
| Greedy function approximation: A gradient boosting machine. | 2001 | 30,137 |
| Classification and Regression Trees. | 1986 | 21,044 |
| Bagging Predictors | 1996 | 17,191 |
| UCI Repository of machine learning databases | 1998 | 10,546 |
| Improved boosting algorithms using confidence-rated predictions | 1998 | 2,573 |
| Boosting the margin: a new explanation for the effectiveness of voting methods | 1998 | 2,344 |
| UCI Repository of Machine Learning Databases | 1996 | 2,343 |
What cites it, inside the corpus
| Paper | Year | Cited |
|---|---|---|
| A survey on ensemble learning | 2019 | 1,951 |
Links
Download BibTeX · Download RIS · DOI · Open access copy · OpenAlex record
Topics
| Imbalanced Data Classification Techniques | Computer Science |
| Machine Learning and Algorithms | Computer Science |
| Machine Learning and Data Classification | Computer Science |
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