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はてなブックマークの新機能における自然言語処理の活用
by Shunsuke Kozawa
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Emacs上のターミナルを最強に
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![AdaBoost [’95 Freund, Schapire]
特徴
◮ 弱分類器の重みと閾値を自動的に決定
◮ 弱分類器は弱すぎなければなんでもよい
◮ 精度が 50%以上なら全体精度も向上
◮ いろんな弱分類器をいい具合に組み合わせ可能
原...](https://image.slidesharecdn.com/tarao2014-12-16adtech-141217210259-conversion-gate02/75/BrandSafe-23-2048.jpg?cb=1666661570)
![Perceptron [’58 Rosenblatt]
特徴
◮ 多次元の入力の重みづけをいい感じにする
◮ 計算がすごく簡単 (実装も簡単)
◮ 教科書に載ってるレベルで枯れている
◮ 近年見直されさまざまな改良が
実装方針
◮ ひとまず単純...](https://image.slidesharecdn.com/tarao2014-12-16adtech-141217210259-conversion-gate02/75/BrandSafe-26-2048.jpg?cb=1666661570)
![Perceptron [’58 Rosenblatt]
— 原理
学習手続
1. 重みベクトルを w = (0, . . ., 0) で初期化
2. 現在の重みでの判定結果 g
3. 本来の正しい判定結果 a ∈ {1, −1}
4. a · ...](https://image.slidesharecdn.com/tarao2014-12-16adtech-141217210259-conversion-gate02/75/BrandSafe-27-2048.jpg?cb=1666661570)
