A model of context-dependent association using selective desensitization of nonmonotonic neural elements
نویسندگان
چکیده
あらまし 分散情報表現を用いた神経回路網において,同じ入力から文脈に応じて異なるパターンを連想する 際には,主に入力パターンに文脈を表すパターンを連結する方法が用いられてきた.しかし,この方法には本質 的な問題点があり,入力及び文脈パターンの数に関して強い制約が生じていた.本論文では,これとは異なる文 脈修飾の方式を非単調神経回路網に適用することによって,従来の問題点を解決する文脈依存的連想モデルを構 築する.このモデルでは,入力及び文脈パターン数に関係なく,学習できる連合の数が素子数にほぼ比例して増 加する.また,文脈を切り換えることによって,アトラクタ間の状態遷移を自在に制御することができる.本モ デルは,素子数や学習時間の爆発を招くことなく任意の有限オートマトンの動作を模擬可能であると同時に,完 全な分散表現に基づく高い汎化能力を備えており,従来の記号処理の限界を打破する可能性を秘めている. キーワ ドー 非単調連想モデル,軌道アトラクタ,積型文脈修飾,文脈主導型連想,有限オートマトン
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ورودعنوان ژورنال:
- Systems and Computers in Japan
دوره 36 شماره
صفحات -
تاریخ انتشار 2005