Presentation 1999/12/16
Minimum Squared Error Estimation of Normal Loglikelihood
Koji Tsuda, Shotaro Akaho,
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Abstract(in Japanese) (See Japanese page)
Abstract(in English) In pattern recognition based on density estimation, it is important to estimate the likelihood at unlabeled samples under the assumption that the underlying distribution is normal. The most frequently used method is the plug-in method, where the mean and variance of normal distribution is estimated from training samples. The plug-in method is simple and can indirectly estimate the likelihood, but the accuracy is poor. We propose to estimate the likelihood directly by minimizing the squared error and show that the accuracy is substantially improved.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) Pattern recognition / Normal loglikelihood / Minimum squared error / Plug-in estimator
Paper # PRMU99-176
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Conference Information
Committee PRMU
Conference Date 1999/12/16(1days)
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Paper Information
Registration To Pattern Recognition and Media Understanding (PRMU)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Minimum Squared Error Estimation of Normal Loglikelihood
Sub Title (in English)
Keyword(1) Pattern recognition
Keyword(2) Normal loglikelihood
Keyword(3) Minimum squared error
Keyword(4) Plug-in estimator
1st Author's Name Koji Tsuda
1st Author's Affiliation Electrotechnical Laboratory()
2nd Author's Name Shotaro Akaho
2nd Author's Affiliation Electrotechnical Laboratory
Date 1999/12/16
Paper # PRMU99-176
Volume (vol) vol.99
Number (no) 514
Page pp.pp.-
#Pages 6
Date of Issue