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Boosting Learning Algorithm for Pattern Recognition and Beyond
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This paper discusses recent developments for pattern recognition focusing on boosting approach in machine learning. The statistical properties such as Bayes risk consistency for several loss functions are discussed in a probabilistic framework. There are a number of loss functions proposed for different purposes and targets. A unified derivation is given by a generator function U which naturally defines entropy, divergence and loss function. The class of U-loss functions associates with the boosting learning algorithms for the loss minimization, which includes AdaBoost and LogitBoost as a twin generated from Kullback-Leibler divergence, and the (partial) area under the ROC curve. We expand boosting to unsupervised learning, typically density estimation employing U-loss function. Finally, a future perspective in machine learning is discussed.
- Publication
- IEICE TRANSACTIONS on Information Vol.E94-D No.10 pp.1863-1869
- Publication Date
- 2011/10/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E94.D.1863
- Type of Manuscript
- Special Section INVITED PAPER (Special Section on Information-Based Induction Sciences and Machine Learning)
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Osamu KOMORI, Shinto EGUCHI, "Boosting Learning Algorithm for Pattern Recognition and Beyond" in IEICE TRANSACTIONS on Information,
vol. E94-D, no. 10, pp. 1863-1869, October 2011, doi: 10.1587/transinf.E94.D.1863.
Abstract: This paper discusses recent developments for pattern recognition focusing on boosting approach in machine learning. The statistical properties such as Bayes risk consistency for several loss functions are discussed in a probabilistic framework. There are a number of loss functions proposed for different purposes and targets. A unified derivation is given by a generator function U which naturally defines entropy, divergence and loss function. The class of U-loss functions associates with the boosting learning algorithms for the loss minimization, which includes AdaBoost and LogitBoost as a twin generated from Kullback-Leibler divergence, and the (partial) area under the ROC curve. We expand boosting to unsupervised learning, typically density estimation employing U-loss function. Finally, a future perspective in machine learning is discussed.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E94.D.1863/_p
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@ARTICLE{e94-d_10_1863,
author={Osamu KOMORI, Shinto EGUCHI, },
journal={IEICE TRANSACTIONS on Information},
title={Boosting Learning Algorithm for Pattern Recognition and Beyond},
year={2011},
volume={E94-D},
number={10},
pages={1863-1869},
abstract={This paper discusses recent developments for pattern recognition focusing on boosting approach in machine learning. The statistical properties such as Bayes risk consistency for several loss functions are discussed in a probabilistic framework. There are a number of loss functions proposed for different purposes and targets. A unified derivation is given by a generator function U which naturally defines entropy, divergence and loss function. The class of U-loss functions associates with the boosting learning algorithms for the loss minimization, which includes AdaBoost and LogitBoost as a twin generated from Kullback-Leibler divergence, and the (partial) area under the ROC curve. We expand boosting to unsupervised learning, typically density estimation employing U-loss function. Finally, a future perspective in machine learning is discussed.},
keywords={},
doi={10.1587/transinf.E94.D.1863},
ISSN={1745-1361},
month={October},}
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TY - JOUR
TI - Boosting Learning Algorithm for Pattern Recognition and Beyond
T2 - IEICE TRANSACTIONS on Information
SP - 1863
EP - 1869
AU - Osamu KOMORI
AU - Shinto EGUCHI
PY - 2011
DO - 10.1587/transinf.E94.D.1863
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E94-D
IS - 10
JA - IEICE TRANSACTIONS on Information
Y1 - October 2011
AB - This paper discusses recent developments for pattern recognition focusing on boosting approach in machine learning. The statistical properties such as Bayes risk consistency for several loss functions are discussed in a probabilistic framework. There are a number of loss functions proposed for different purposes and targets. A unified derivation is given by a generator function U which naturally defines entropy, divergence and loss function. The class of U-loss functions associates with the boosting learning algorithms for the loss minimization, which includes AdaBoost and LogitBoost as a twin generated from Kullback-Leibler divergence, and the (partial) area under the ROC curve. We expand boosting to unsupervised learning, typically density estimation employing U-loss function. Finally, a future perspective in machine learning is discussed.
ER -
English
