Corpus ID: 1836349

Experiments with a New Boosting Algorithm

@inproceedings{Freund1996ExperimentsWA,
  title={Experiments with a New Boosting Algorithm},
  author={Y. Freund and R. Schapire},
  booktitle={ICML},
  year={1996}
}
  • Y. Freund, R. Schapire
  • Published in ICML 1996
  • Computer Science
  • In an earlier paper, we introduced a new "boosting" algorithm called AdaBoost which, theoretically, can be used to significantly reduce the error of any learning algorithm that con- sistently generates classifiers whose performance is a little better than random guessing. [...] Key Result In the second set of experiments, we studied in more detail the performance of boosting using a nearest-neighbor classifier on an OCR problem.Expand Abstract
    7,968 Citations
    A New Boosting Algorithm Using Input-Dependent Regularizer
    • 73
    • PDF
    Improved Boosting Algorithms Using Confidence-rated Predictions
    • 1,486
    • PDF
    Improved Boosting Algorithms using Confidence-Rated Predictions
    • 2,060
    • PDF
    Boosting Neural Networks
    • 250
    • Highly Influenced
    • PDF
    Stopping Criterion for Boosting-Based Data Reduction Techniques: from Binary to Multiclass Problem
    • 37
    • PDF
    Supervised projection approach for boosting classifiers
    • 20
    • Highly Influenced
    • PDF
    An Empirical Boosting Scheme for ROC-Based Genetic Programming Classifiers
    Quadratic boosting
    • 10
    • PDF
    Training Methods for Adaptive Boosting of Neural Networks
    • 61
    • Highly Influenced
    • PDF

    References

    SHOWING 1-10 OF 35 REFERENCES
    A decision-theoretic generalization of on-line learning and an application to boosting
    • 11,982
    • PDF
    Improving Performance in Neural Networks Using a Boosting Algorithm
    • 193
    • PDF
    A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting
    • 6,407
    • PDF
    Applying the Waek Learning Framework to Understand and Improve C4.5
    • 111
    • PDF
    On the boosting ability of top-down decision tree learning algorithms
    • 128
    • PDF
    Boosting and Other Ensemble Methods
    • 330
    Bias, Variance , And Arcing Classifiers
    • 568
    • Highly Influential
    • PDF
    Boosting Decision Trees
    • 249
    C4.5: Programs for Machine Learning
    • 20,929
    Boosting Performance in Neural Networks
    • 230