@techreport{oai:ipsj.ixsq.nii.ac.jp:00208746,
 author = {雲居, 玄道 and 八木, 秀樹 and 小林, 学 and 後藤, 正幸 and 平澤, 茂一 and Gendo, Kumoi and Hideki, Yagi and Manabu, Kobayashi and Masayuki, Goto and Shigeichi, Hirasawa},
 issue = {9},
 month = {Dec},
 note = {与えられた二値判分類器を組合せて用いる多値分類器の構成法の 1 つに，符号理論の枠組みを導入した誤り訂正符号に基づく多値分類法（Error-Correcting Output Coding：ECOC法）がある．この手法が実データに対して良い性能を示すことは実験的に知られているが，ECOC 法に対する分類精度について，理論的な最適性については明らかになっていない．そこで本研究では最大事後確率分類を可能とする二値分類器を仮定した場合，ECOC 法が最適な多値分類法になる十分条件を示す．この結果，同様の仮定のもとで n-vs-all 及び Exhaustive 符号が最適な多値分類法になることが示せる．これは種々の ECOC 法に対する最適性の議論の方向性の一つを示唆している．, One of the methods for constructing a multi-valued classifier that uses a combination of given two-valued classifiers is the Error-Correcting Output Coding (ECOC) method, which is based on error-correcting codes introducing a code theory framework. Although it is experimentally known that this method performs well on real data, the theoretical optimality of the classification accuracy for the ECOC method has not been clarified. In this study, we show sufficient conditions for the ECOC method to be an optimal multi-valued classification method under the assumption that binary classifiers achieve maximum posterior probability classification. As a result, we can show that n-vs-all and Exhaustive signs are the best multi-valued classification method under the same assumptions. This suggests one of the directions of the optimization debate for various ECOC methods.},
 title = {多値分類問題におけるECOC法の最適性に関する一考察},
 year = {2020}
}