Combinations of Weak Classifiers

NeurIPS 1996 pp. 494-500

/neurips/1996/ji1996neurips-combinations/

Abstract

To obtain classification systems with both good generalization per(cid:173) formance and efficiency in space and time, we propose a learning method based on combinations of weak classifiers, where weak clas(cid:173) sifiers are linear classifiers (perceptrons) which can do a little better than making random guesses. A randomized algorithm is proposed to find the weak classifiers. They· are then combined through a ma(cid:173) jority vote. As demonstrated through systematic experiments, the method developed is able to obtain combinations of weak classifiers with good generalization performance and a fast training time on a variety of test problems and real applications.

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Text

Ji and Ma. "Combinations of Weak Classifiers." Neural Information Processing Systems, 1996.

Markdown

[Ji and Ma. "Combinations of Weak Classifiers." Neural Information Processing Systems, 1996.](https://mlanthology.org/neurips/1996/ji1996neurips-combinations/)

BibTeX

@inproceedings{ji1996neurips-combinations,
  title     = {{Combinations of Weak Classifiers}},
  author    = {Ji, Chuanyi and Ma, Sheng},
  booktitle = {Neural Information Processing Systems},
  year      = {1996},
  pages     = {494-500},
  url       = {https://mlanthology.org/neurips/1996/ji1996neurips-combinations/}
}