Artificial Prediction Markets for Online Prediction
Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence. Doctoral Consortium. Pages 4371-4371.
Abstract
In this dissertation, we propose an online learning technique to predict a value of a continuous variable by (i) integrating a set of data streams from heterogeneous sources with time varying compositions including (a) changing the quality of data streams, (b) addition or deletion of data streams (ii) integrating the results of several analysis algorithms for each data source when the most suitable algorithm for a given data source is not known a priori (iii) dynamically weighting the prediction of each analysis algorithm and data source on the system prediction based on their varying quality.
@inproceedings{ijcai2015p633,
title = {Artificial Prediction Markets for Online Prediction},
author = {Jahedpari, Fatemeh},
booktitle = {Proceedings of the Twenty-Fourth International Joint Conference on
Artificial Intelligence, {IJCAI-15}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Qiang Yang and Michael Wooldridge},
pages = {4371--4371},
year = {2015},
month = {7},
note = {Doctoral Consortium},
url = {https://ijcai.org/proceedings/2015/633},
}
TY - CPAPER TI - Artificial Prediction Markets for Online Prediction AU - Jahedpari, Fatemeh T2 - Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-15 PB - International Joint Conferences on Artificial Intelligence PY - 2015 SP - 4371 EP - 4371 UR - https://ijcai.org/proceedings/2015/633 ER -Copied
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