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Proceedings of the 4th ACM conference on Electronic commerce
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The Journal of Machine Learning Research
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Pattern Analysis & Applications
ICML '04 Proceedings of the twenty-first international conference on Machine learning
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The Journal of Machine Learning Research
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The Journal of Machine Learning Research
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A new understanding of prediction markets via no-regret learning
Proceedings of the 11th ACM conference on Electronic commerce
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CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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Prediction markets are used in real life to predict outcomes of interest such as presidential elections. This paper presents a mathematical theory of artificial prediction markets for supervised learning of conditional probability estimators. The artificial prediction market is a novel method for fusing the prediction information of features or trained classifiers, where the fusion result is the contract price on the possible outcomes. The market can be trained online by updating the participants' budgets using training examples. Inspired by the real prediction markets, the equations that govern the market are derived from simple and reasonable assumptions. Efficient numerical algorithms are presented for solving these equations. The obtained artificial prediction market is shown to be a maximum likelihood estimator. It generalizes linear aggregation, existent in boosting and random forest, as well as logistic regression and some kernel methods. Furthermore, the market mechanism allows the aggregation of specialized classifiers that participate only on specific instances. Experimental comparisons show that the artificial prediction markets often outperform random forest and implicit online learning on synthetic data and real UCI data sets. Moreover, an extensive evaluation for pelvic and abdominal lymph node detection in CT data shows that the prediction market improves adaboost's detection rate from 79.6% to 81.2% at 3 false positives/volume.