Improved Boosting Algorithms Using Confidence-rated Predictions
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Summary
Several improvements to Freund and Schapire's AdaBoost boosting algorithm are described, particularly in a setting in which hypotheses may assign confidences to each of their predictions.
- Type
- article
- Published
- 1998-07-24
- Cited by
- 3,648
- References
- 33
- Access
- Open access
- OpenAlex
- https://openalex.org/W2032210760
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:2329907
Keywords
Boosting (machine learning), Computer science, Confidence interval, Artificial intelligence, Machine learning
References
- Special Invited Paper-Additive logistic regression: A statistical view of boosting
- Pruning Adaptive Boosting
- Using output codes to boost multiclass learning problems
- Solving Multiclass Learning Problems via Error-Correcting Output Codes
- An Empirical Evaluation of Bagging and Boosting
- Boosting the margin: A new explanation for the effectiveness of voting methods
- A Generalization of Sauer's Lemma
- A decision-theoretic generalization of on-line learning and an application to boosting
- Using and combining predictors that specialize
- On the boosting ability of top-down decision tree learning algorithms
- Arcing classifier (with discussion and a rejoinder by the author)
- Practical Methods of Optimization: Fletcher/Practical Methods of Optimization
- Decision Theoretic Generalizations of the PAC Model for Neural Net and Other Learning Applications
- UCI Repository of machine learning databases
- The Sample Complexity of Pattern Classification with Neural Networks: The Size of the Weights is More Important than the Size of the Network
- Boosting Decision Trees
- Training Methods for Adaptive Boosting of Neural Networks
- What Size Net Gives Valid Generalization?
- Experiments with a New Boosting Algorithm
- UCI Repository of Machine Learning Databases
Cited by
- Surgical tools localization in 3D ultrasound images
- Foundations of Machine Learning
- Accelerating AdaBoost using UCB
- Hypothesis assessments as guidance for incremental and meta-learning
- Detecting and highlighting text in images
- Importance Sampled Learning Ensembles
- Labeling hypergraph-structured data using markov network
- TRECVID 2007 by the Brno Group.
- Multiclass Adaboost Based on an Ensemble of Binary AdaBoosts
- Guess-Averse Loss Functions For Cost-Sensitive Multiclass Boosting
- Spatially Aggregated Multi-Class Pattern Classification in Functional MRI using Optimally Selected Functional Brain Areas
- Modeling Mobile User Behavior for Anomaly Detection
- Ensemble Methods for Structured Prediction
- Sign language recognition : Generalising to more complex corpora
- Feed Distillation Using AdaBoost and Topic Maps
- Learning to Classify Email into “Speech Acts”
- Designing neural network committees by combining boosting ensembles
- Image Classification by Multi-Class Boosting of Visual and Infrared Fusion with Applications to Object Pose Recognition
- Various Approaches to Web Information Processing
- Experimental Evaluation of a People Detection Algorithm in Dynamic Environments
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