Super-structure: a decisive framework to rationalize and improve both optimal and heuristic Bayesian network structure learning algorithms (バイオ情報学)
スポンサーリンク
概要
- 論文の詳細を見る
データに基づくベイジアンネットワークの構造学習において精度向上は,遺伝子ネットワークのような巨大システムをモデル化する際に極めて重要となる.この目的のため,我々は,スコア関数に基づく探索に無向グラフによるsuper-structureを制約として用いる方法を提案する.つまり,探索する枝はsuper-structureに含まれているものに限る.さらに,データを用いたsuper-structureの近似法を導入し,制約付き最適アルゴリズム(COS)を構成する.COSにより,最適アルゴリズムはより大きなネットワークに適用可能となる.また,トポロジカル順序に基づく発見的アルゴリズム(PERM)を導出できる.数値実験により,提案するアルゴリズムは,他の方法よりも精度の面で優れていることを示した.
- 2008-03-03
著者
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MIYANO Satoru
Human Genome Center, The Institute of Medical Science, University of Tokyo
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Miyano Satoru
Human Genome Center Institute Of Medical Science The University Of Tokyo
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Miyano Satoru
Human Genome Center Institute Of Medical Science University Of Tokyo
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IMOTO Seiya
Human Genome Center, Institute of Medical Science, University of Tokyo
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Imoto Seiya
Human Genome Center Institute Of Medical Science University Of Tokyo
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Perrier Eric
Human Genome Center, Institute of Medical Science, University of Tokyo
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Perrier Eric
Human Genome Center Institute Of Medical Science University Of Tokyo
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