Abstract
本稿では，相対的な順位の高低を表す順序ラベルと特徴ベクトルで表現された事例ペアを元に，事例の順位を予測する教師あり自己組織化マップアルゴリズムであるOrderSOMを提案する．提案手法では事例ペアに対して選択されたBMU (Best Matching Unit) の競合層における位置情報と順序ラベルに基づいてBMUの位置を修正することで，事例の順位を予測する順序学習を実現する．人工データと実データを用いた評価実験を通じて，提案手法の有効性を検証した．
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