| Budget Amount *help |
¥15,470,000 (Direct Cost: ¥11,900,000、Indirect Cost: ¥3,570,000)
Fiscal Year 2022: ¥2,730,000 (Direct Cost: ¥2,100,000、Indirect Cost: ¥630,000)
Fiscal Year 2021: ¥3,640,000 (Direct Cost: ¥2,800,000、Indirect Cost: ¥840,000)
Fiscal Year 2020: ¥2,080,000 (Direct Cost: ¥1,600,000、Indirect Cost: ¥480,000)
Fiscal Year 2019: ¥3,510,000 (Direct Cost: ¥2,700,000、Indirect Cost: ¥810,000)
Fiscal Year 2018: ¥3,510,000 (Direct Cost: ¥2,700,000、Indirect Cost: ¥810,000)
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| Outline of Final Research Achievements |
As a research result to evaluate the degree of matching between estimated class boundaries and the ideal Bayes boundary, the following methods are developed: the BBS (Bayes Boundary-based optimal pattern classifier Selection ) method that selects an optimal recognizer from a large number of pattern recognizer candidates, its improved version that achieves 10,000 times speed-up, the MBB (Maximum Bayes Boundary-ness) method that directly enhances the Bayes boundary property of recognizers through the loss minimization, and an improved version of the MBB method of which achieved error rate can be very close to the theoretical minimum classification error rate. All of these methods are evaluated in fixed-dimensional pattern recognition tasks. Moreover, the CS-ACELP (Conjugate Structure-Algebraic Code Excited Linear Prediction) method is used to recognize variable-dimensional patterns such as speech, and we find that its recognition rates can be close to the Bayes error of used data.
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