Non-logarithmic information measures, α-weighted em algorithms and speedup of learning

Y. Matsuyama

doi:10.1109/ISIT.1998.708990

Non-logarithmic information measures, α-weighted em algorithms and speedup of learning

Y. Matsuyama^*

^*この研究の対応する著者

研究成果: Conference contribution

3 被引用数 (Scopus)

抄録

Starting from Renyi's α-divergence, a class of generalized EM algorithms called the α-EM algorithms of the WEM algorithms are derived. Merits of this generalization are found on speedup of learning, i.e., acceleration of convergence. Discussions include novel α-versions of logarithm, efficient scores, information matrices and the Cramer-Rao bound. The speedup is examined on Gaussian mixture learning systems.

本文言語	English
ホスト出版物のタイトル	IEEE International Symposium on Information Theory - Proceedings
ページ	385
ページ数	1
DOI	https://doi.org/10.1109/ISIT.1998.708990
出版ステータス	Published - 1998
イベント	1998 IEEE International Symposium on Information Theory, ISIT 1998 - Cambridge, MA 継続期間: 1998 8月 16 → 1998 8月 21

Other

Other	1998 IEEE International Symposium on Information Theory, ISIT 1998
City	Cambridge, MA
Period	98/8/16 → 98/8/21

ASJC Scopus subject areas

応用数学
モデリングとシミュレーション
理論的コンピュータサイエンス
情報システム

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10.1109/ISIT.1998.708990

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引用スタイル

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AB - Starting from Renyi's α-divergence, a class of generalized EM algorithms called the α-EM algorithms of the WEM algorithms are derived. Merits of this generalization are found on speedup of learning, i.e., acceleration of convergence. Discussions include novel α-versions of logarithm, efficient scores, information matrices and the Cramer-Rao bound. The speedup is examined on Gaussian mixture learning systems.

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T2 - 1998 IEEE International Symposium on Information Theory, ISIT 1998

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Non-logarithmic information measures, α-weighted em algorithms and speedup of learning

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Other

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