A learning algorithm of compressed candidates based on Bayesia belief network is developed to solve slow running problem of traditional Bayesian belief network constructing algorithm.

 
  • 摘要針對傳統算法分類(lèi)速度較慢的不足,改進(jìn)傳統算法中候選變量的搜索方式,提出用依賴(lài)度量函數測量變量之間的依賴(lài)程度,得出壓縮候選的貝葉斯信念網(wǎng)絡(luò )構造算法。
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目錄 附錄 查詞歷史
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