TY - GEN
T1 - A model selection method based on bound of learning coefficient
AU - Yamazaki, Keisuke
AU - Nagata, Kenji
AU - Watanabe, Sumio
AU - Müller, Klaus Robert
PY - 2006
Y1 - 2006
N2 - To decide the optimal size of learning machines is a central issue in the statistical learning theory, and that is why some theoretical criteria such as the BIG are developed. However, they cannot be applied to singular machines, and it is known that many practical learning machines e.g. mixture models, hidden Markov models, and Bayesian networks, are singular. Recently, we proposed the Singular Information Criterion (SingIC), which allows us to select the optimal size of singular machines. The SingIC is based on the analysis of the learning coefficient. So, the machines, to which the SingIC can be applied, are still limited. In this paper, we propose an extension of this criterion, which enables us to apply it to many singular machines, and evaluate the efficiency in Gaussian mixtures. The results offer an effective strategy to select the optimal size.
AB - To decide the optimal size of learning machines is a central issue in the statistical learning theory, and that is why some theoretical criteria such as the BIG are developed. However, they cannot be applied to singular machines, and it is known that many practical learning machines e.g. mixture models, hidden Markov models, and Bayesian networks, are singular. Recently, we proposed the Singular Information Criterion (SingIC), which allows us to select the optimal size of singular machines. The SingIC is based on the analysis of the learning coefficient. So, the machines, to which the SingIC can be applied, are still limited. In this paper, we propose an extension of this criterion, which enables us to apply it to many singular machines, and evaluate the efficiency in Gaussian mixtures. The results offer an effective strategy to select the optimal size.
UR - http://www.scopus.com/inward/record.url?scp=33749838218&partnerID=8YFLogxK
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U2 - 10.1007/11840930_38
DO - 10.1007/11840930_38
M3 - Conference contribution
AN - SCOPUS:33749838218
SN - 3540388710
SN - 9783540388715
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 371
EP - 380
BT - Artificial Neural Networks, ICANN 2006 - 16th International Conference, Proceedings
PB - Springer Verlag
T2 - 16th International Conference on Artificial Neural Networks, ICANN 2006
Y2 - 10 September 2006 through 14 September 2006
ER -