TY - GEN
T1 - Learning voting trees
AU - Procaccia, Ariel D.
AU - Zohar, Aviv
AU - Peleg, Yoni
AU - Rosenschein, Jeffrey S.
PY - 2007
Y1 - 2007
N2 - Binary voting trees provide a succinct representation for a large and prominent class of voting rules. In this paper, we investigate the PAC-learnability of this class of rules. We show that, while in general a learning algorithm would require an exponential number of samples, if the number of leaves is polynomial in the size of the set of alternatives then a polynomial training set suffices. We apply these results in an emerging theory: automated design of voting rules by learning.
AB - Binary voting trees provide a succinct representation for a large and prominent class of voting rules. In this paper, we investigate the PAC-learnability of this class of rules. We show that, while in general a learning algorithm would require an exponential number of samples, if the number of leaves is polynomial in the size of the set of alternatives then a polynomial training set suffices. We apply these results in an emerging theory: automated design of voting rules by learning.
UR - http://www.scopus.com/inward/record.url?scp=36348938731&partnerID=8YFLogxK
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AN - SCOPUS:36348938731
SN - 1577353234
SN - 9781577353232
T3 - Proceedings of the National Conference on Artificial Intelligence
SP - 110
EP - 115
BT - AAAI-07/IAAI-07 Proceedings
T2 - AAAI-07/IAAI-07 Proceedings: 22nd AAAI Conference on Artificial Intelligence and the 19th Innovative Applications of Artificial Intelligence Conference
Y2 - 22 July 2007 through 26 July 2007
ER -