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indicator kriging in Chinese

Pronunciation:
How to pronounce "indicator kriging""indicator kriging" in a sentence

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  • 克利金法

Examples

  • Indicator kriging study of multivariate information in temporal - spatial domain
    時空多元指示克立格法的理論研究
  • For each point x in the feature space , the proposed algorithm calculates the probabilities that x belongs to each individual classes using indicator kriging , and then assign x to the class with highest probability
    研究中我們將各種類別在特徵空間之分布視為一個指標變數(shù)之隨機變域,并發(fā)展一個以指標變域空間推估為基礎之新的分類理論。
  • The analysis and design class diagram are given . the sequence indication stochastic simulation algorithm based on indicator kriging is realized . the algorithm applies to discrete and continuous variable has no restrict to data distribution
    系統(tǒng)給出了沉積相隨機建模的分析類圖和設計類圖,實現(xiàn)了基于指示克里金估計的序貫指示隨機模擬算法,該算法對離散數(shù)據(jù)和連續(xù)數(shù)據(jù)都適用,不要求數(shù)據(jù)的分布特征,可以較好的模擬相特征。
  • Synthesizing multivariate information , au - comprehensive chemical exploratory anomaly was discussed with geostatistical methods at guilaizhuang au - deposit . main work included : using cokriging , influence of regional chemical elements w , as , sb to au - anomaly and au - mineralization has been studied . contribution of g au ag to au - anomaly has been probed into making use of indicator kriging and some suggestions for the future work have been given . key words : multivariate information ; au - comprehensive chemical exploratory anomaly ; geostatistics ; cokriging ; indicator kriging
    綜合多元信息,對歸來莊金礦床綜合化探金異常進行地質(zhì)統(tǒng)計學研究,主要作了如下工作:利用協(xié)同克立格法研究化探元素w , as , sb對au異常和au成礦的影響,利用指示克立格法研究g au ag對形成au異常的貢獻,并對今后工作提出建議。
  • The proposed indicator kriging classification algorithm has the following advantages : ( 1 ) it can deal with anisotropic problem in feature space , ( 2 ) it is a nonparametric method , and need not to know the type of probability distribution , and ( 3 ) it yields 100 % classification accuracy for the training data
    此分類法具有如下之優(yōu)點: ( 1 )可處理特征空間中非等向分布之問題, ( 2 )該方法屬于無母數(shù)分類法,不需假設各類特征之機率分布類型, ( 3 )對訓練像元之分類正確率可達100 。
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