TY - GEN
T1 - Design of an unsupervised weight parameter estimation method in ensemble learning
AU - Uchida, Masato
AU - Maehara, Yousuke
AU - Shioya, Hiroyuki
PY - 2008/10/27
Y1 - 2008/10/27
N2 - A learning method using an integration of multiple component predictors as an ultimate predictor is generically referred to as ensemble learning. The present paper proposes a weight parameter estimation method for ensemble learning under the constraint that we do not have any information of the desirable (true) output. The proposed method is naturally derived from a mathematical model of ensemble learning, which is based on an exponential mixture type probabilistic model and Kullback divergence. The proposed method provides a legitimate strategy for weight parameter estimation under the abovementioned constraint if it is assumed that the accuracy of all multiple predictors are the same. We verify the effectiveness of the proposed method through numerical experiments.
AB - A learning method using an integration of multiple component predictors as an ultimate predictor is generically referred to as ensemble learning. The present paper proposes a weight parameter estimation method for ensemble learning under the constraint that we do not have any information of the desirable (true) output. The proposed method is naturally derived from a mathematical model of ensemble learning, which is based on an exponential mixture type probabilistic model and Kullback divergence. The proposed method provides a legitimate strategy for weight parameter estimation under the abovementioned constraint if it is assumed that the accuracy of all multiple predictors are the same. We verify the effectiveness of the proposed method through numerical experiments.
UR - http://www.scopus.com/inward/record.url?scp=54249116402&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=54249116402&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-69158-7_80
DO - 10.1007/978-3-540-69158-7_80
M3 - Conference contribution
AN - SCOPUS:54249116402
SN - 3540691545
SN - 9783540691549
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 771
EP - 780
BT - Neural Information Processing - 14th International Conference, ICONIP 2007, Revised Selected Papers
T2 - 14th International Conference on Neural Information Processing, ICONIP 2007
Y2 - 13 November 2007 through 16 November 2007
ER -